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Journal articles on the topic 'Sparse features'
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Caragea, Cornelia, Adrian Silvescu, and Prasenjit Mitra. "Combining Hashing and Abstraction in Sparse High Dimensional Feature Spaces." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 3–9. http://dx.doi.org/10.1609/aaai.v26i1.8117.
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With the exponential increase in the number of documents available online, e.g., news articles, weblogs, scientific documents, the development of effective and efficient classification methods is needed. The performance of document classifiers critically depends, among other things, on the choice of the feature representation. The commonly used "bag of words" and n-gram representations can result in prohibitively high dimensional input spaces. Data mining algorithms applied to these input spaces may be intractable due to the large number of dimensions. Thus, dimensionality reduction algorithms that can process data into features fast at runtime, ideally in constant time per feature, are greatly needed in high throughput applications, where the number of features and data points can be in the order of millions. One promising line of research to dimensionality reduction is feature clustering. We propose to combine two types of feature clustering, namely hashing and abstraction based on hierarchical agglomerative clustering, in order to take advantage of the strengths of both techniques. Experimental results on two text data sets show that the combined approach uses significantly smaller number of features and gives similar performance when compared with the "bag of words" and n-gram approaches.
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Simion, Georgiana. "Sparse Features for Finger Detection." Advanced Engineering Forum 8-9 (June 2013): 535–42. http://dx.doi.org/10.4028/www.scientific.net/aef.8-9.535.
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The use of hand as direct input device evolved with the development of Natural User Interfaces (NUI). Touch screens are well integrated in our daily life and the new challenge is to implement interfaces which involve no direct contact. This paper presents such a solution implemented within the framework of sparse techniques. The feature vectors representing key distances and angles are extracted and used to detect fingers. The experimental results have demonstrated that this technique is able to obtain an error rate about 5% in finger detection.
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Kronvall, Ted, Maria Juhlin, Johan Swärd, Stefan I. Adalbjörnsson, and Andreas Jakobsson. "Sparse modeling of chroma features." Signal Processing 130 (January 2017): 105–17. http://dx.doi.org/10.1016/j.sigpro.2016.06.020.
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He, Wangpeng, Peipei Zhang, Xuan Liu, Binqiang Chen, and Baolong Guo. "Group-Sparse Feature Extraction via Ensemble Generalized Minimax-Concave Penalty for Wind-Turbine-Fault Diagnosis." Sustainability 14, no. 24 (December 14, 2022): 16793. http://dx.doi.org/10.3390/su142416793.
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Extracting weak fault features from noisy measured signals is critical for the diagnosis of wind turbine faults. In this paper, a novel group-sparse feature extraction method via an ensemble generalized minimax-concave (GMC) penalty is proposed for machinery health monitoring. Specifically, the proposed method tackles the problem of formulating large useful magnitude values as isolated features in the original GMC-based sparse feature extraction method. To accurately estimate group-sparse fault features, the proposed method formulates an effective unconstrained optimization problem wherein the group-sparse structure is incorporated into non-convex regularization. Moreover, the convex condition is proved to maintain the convexity of the whole formulated cost function. In addition, the setting criteria of the regularization parameter are investigated. A simulated signal is presented to verify the performance of the proposed method for group-sparse feature extraction. Finally, the effectiveness of the proposed group-sparse feature extraction method is further validated by experimental fault diagnosis cases.
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Banihashem, Kiarash, Mohammad Hajiaghayi, and Max Springer. "Optimal Sparse Recovery with Decision Stumps." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 6745–52. http://dx.doi.org/10.1609/aaai.v37i6.25827.
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Decision trees are widely used for their low computational cost, good predictive performance, and ability to assess the importance of features. Though often used in practice for feature selection, the theoretical guarantees of these methods are not well understood. We here obtain a tight finite sample bound for the feature selection problem in linear regression using single-depth decision trees. We examine the statistical properties of these "decision stumps" for the recovery of the s active features from p total features, where s
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Xing, Zhan, Jianhui Lin, Yan Huang, and Cai Yi. "A Feature Extraction Method of Wheelset-Bearing Fault Based on Wavelet Sparse Representation with Adaptive Local Iterative Filtering." Shock and Vibration 2020 (July 25, 2020): 1–20. http://dx.doi.org/10.1155/2020/2019821.
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The feature extraction of wheelset-bearing fault is important for the safety service of high-speed train. In recent years, sparse representation is gradually applied to the fault diagnosis of wheelset-bearing. However, it is difficult for traditional sparse representation to extract fault features ideally when some strong interference components are imposed on the signal. Therefore, this paper proposes a novel feature extraction method of wheelset-bearing fault based on the wavelet sparse representation with adaptive local iterative filtering. In this method, the adaptive local iterative filtering reduces the impact of interference components effectively and contributes to the extraction of sparse impulses. The wavelet sparse representation, which adopts L1-regularized optimization for a globally optimal solution in sparse coding, extracts intrinsic features of fault in the wavelet domain. To validate the effectiveness of this proposed method, both simulated signals and experimental signals are analyzed. The results show that the fault features of wheelset-bearing are sufficiently extracted by the proposed method.
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Wei, Wang, Tang Can, Wang Xin, Luo Yanhong, Hu Yongle, and Li Ji. "Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation." Computational Intelligence and Neuroscience 2019 (November 21, 2019): 1–9. http://dx.doi.org/10.1155/2019/8258275.
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An image object recognition approach based on deep features and adaptive weighted joint sparse representation (D-AJSR) is proposed in this paper. D-AJSR is a data-lightweight classification framework, which can classify and recognize objects well with few training samples. In D-AJSR, the convolutional neural network (CNN) is used to extract the deep features of the training samples and test samples. Then, we use the adaptive weighted joint sparse representation to identify the objects, in which the eigenvectors are reconstructed by calculating the contribution weights of each eigenvector. Aiming at the high-dimensional problem of deep features, we use the principal component analysis (PCA) method to reduce the dimensions. Lastly, combined with the joint sparse model, the public features and private features of images are extracted from the training sample feature set so as to construct the joint feature dictionary. Based on the joint feature dictionary, sparse representation-based classifier (SRC) is used to recognize the objects. Experiments on face images and remote sensing images show that D-AJSR is superior to the traditional SRC method and some other advanced methods.
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YANG, B. J. "DOMINANT EIGENVECTOR AND EIGENVALUE ALGORITHM IN SPARSE NETWORK SPECTRAL CLUSTERING." Latin American Applied Research - An international journal 48, no. 4 (October 31, 2018): 323–28. http://dx.doi.org/10.52292/j.laar.2018.248.
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The sparse network spectrum clustering problem is studied in this paper. It tries to analyze and improve the sparse network spectrum clustering algorithm from the main feature pair algorithm. The main feature pair algorithm in the matrix calculation is combined with the spectral clustering algorithm to explore the application of the main feature pair algorithm on the network adjacency matrix. The defects of traditional main features are analyzed when the algorithm Power is used on the network of special structural features, and the advantages of the new algorithm SII algorithm is proved. The sparse network spectral clustering algorithm in this paper is based on the Score algorithm, and the main features of the algorithm are refined, analyzed and improved.
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SUN, JUN, WENYUAN WANG, QING ZHUO, and CHENGYUAN MA. "DISCRIMINATORY SPARSE CODING AND ITS APPLICATION TO FACE RECOGNITION." International Journal of Image and Graphics 03, no. 03 (July 2003): 503–21. http://dx.doi.org/10.1142/s0219467803001135.
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Feature extraction is very important in the subject of pattern recognition. Sparse coding is an approach for extracting the independent features of an image. The image features extracted by sparse coding have led to better recognition performance as compared to those from traditional PCA-based methods. A new discriminatory sparse coding (DSC) algorithm is proposed in this paper to further improve the classification performance. Based on reinforcement learning, DSC encodes the training samples by individual class rather than by individual image as in sparse coding. Having done that it will produce a set of features with large and small intraclass variations, which is very suitable for recognition tasks. Experiments are performed on face image feature extraction and recognition. Compared with the traditional PCA- and ICA-based methods, DSC shows a much better recognition performance.
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Grimes, David B., and Rajesh P. N. Rao. "Bilinear Sparse Coding for Invariant Vision." Neural Computation 17, no. 1 (January 1, 2005): 47–73. http://dx.doi.org/10.1162/0899766052530893.
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Recent algorithms for sparse coding and independent component analysis (ICA) have demonstrated how localized features can be learned from natural images. However, these approaches do not take image transformations into account. We describe an unsupervised algorithm for learning both localized features and their transformations directly from images using a sparse bilinear generative model. We show that from an arbitrary set of natural images, the algorithm produces oriented basis filters that can simultaneously represent features in an image and their transformations. The learned generative model can be used to translate features to different locations, thereby reducing the need to learn the same feature at multiple locations, a limitation of previous approaches to sparse coding and ICA. Our results suggest that by explicitly modeling the interaction between local image features and their transformations, the sparse bilinear approach can provide a basis for achieving transformation-invariant vision.
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Anwar, Shahzad, Qingjie Zhao, Muhammad Farhan Manzoor, and Saqib Ishaq Khan. "Saliency Detection Using Sparse and Nonlinear Feature Representation." Scientific World Journal 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/137349.
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An important aspect of visual saliency detection is how features that form an input image are represented. A popular theory supports sparse feature representation, an image being represented with a basis dictionary having sparse weighting coefficient. Another method uses a nonlinear combination of image features for representation. In our work, we combine the two methods and propose a scheme that takes advantage of both sparse and nonlinear feature representation. To this end, we use independent component analysis (ICA) and covariant matrices, respectively. To compute saliency, we use a biologically plausible center surround difference (CSD) mechanism. Our sparse features are adaptive in nature; the ICA basis function are learnt at every image representation, rather than being fixed. We show that Adaptive Sparse Features when used with a CSD mechanism yield better results compared to fixed sparse representations. We also show that covariant matrices consisting of nonlinear integration of color information alone are sufficient to efficiently estimate saliency from an image. The proposed dual representation scheme is then evaluated against human eye fixation prediction, response to psychological patterns, and salient object detection on well-known datasets. We conclude that having two forms of representation compliments one another and results in better saliency detection.
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Feng, Shang, Haifeng Li, Lin Ma, and Zhongliang Xu. "An EEG Feature Extraction Method Based on Sparse Dictionary Self-Organizing Map for Event-Related Potential Recognition." Algorithms 13, no. 10 (October 13, 2020): 259. http://dx.doi.org/10.3390/a13100259.
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In the application of the brain-computer interface, feature extraction is an important part of Electroencephalography (EEG) signal classification. Using sparse modeling to extract EEG signal features is a common approach. However, the features extracted by common sparse decomposition methods are only of analytical meaning, and cannot relate to actual EEG waveforms, especially event-related potential waveforms. In this article, we propose a feature extraction method based on a self-organizing map of sparse dictionary atoms, which can aggregate event-related potential waveforms scattered inside an over-complete sparse dictionary into the code book of neurons in the self-organizing map network. Then, the cosine similarity between the EEG signal sample and the code vector is used as the classification feature. Compared with traditional feature extraction methods based on sparse decomposition, the classification features obtained by this method have more intuitive electrophysiological meaning. The experiment conducted on a public auditory event-related potential (ERP) brain-computer interface dataset showed that, after the self-organized mapping of dictionary atoms, the neurons’ code vectors in the self-organized mapping network were remarkably similar to the ERP waveform obtained after superposition and averaging. The feature extracted by the proposed method used a smaller amount of data to obtain classification accuracy comparable to the traditional method.
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Sun, Zhenzhen, and Yuanlong Yu. "Fast Approximation for Sparse Coding with Applications to Object Recognition." Sensors 21, no. 4 (February 19, 2021): 1442. http://dx.doi.org/10.3390/s21041442.
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Sparse Coding (SC) has been widely studied and shown its superiority in the fields of signal processing, statistics, and machine learning. However, due to the high computational cost of the optimization algorithms required to compute the sparse feature, the applicability of SC to real-time object recognition tasks is limited. Many deep neural networks have been constructed to low fast estimate the sparse feature with the help of a large number of training samples, which is not suitable for small-scale datasets. Therefore, this work presents a simple and efficient fast approximation method for SC, in which a special single-hidden-layer neural network (SLNNs) is constructed to perform the approximation task, and the optimal sparse features of training samples exactly computed by sparse coding algorithm are used as ground truth to train the SLNNs. After training, the proposed SLNNs can quickly estimate sparse features for testing samples. Ten benchmark data sets taken from UCI databases and two face image datasets are used for experiment, and the low root mean square error (RMSE) results between the approximated sparse features and the optimal ones have verified the approximation performance of this proposed method. Furthermore, the recognition results demonstrate that the proposed method can effectively reduce the computational time of testing process while maintaining the recognition performance, and outperforms several state-of-the-art fast approximation sparse coding methods, as well as the exact sparse coding algorithms.
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Li, Jiaye, Guoqiu Wen, Jiangzhang Gan, Leyuan Zhang, and Shanwen Zhang. "Sparse Nonlinear Feature Selection Algorithm via Local Structure Learning." Emerging Science Journal 3, no. 2 (April 9, 2019): 115. http://dx.doi.org/10.28991/esj-2019-01175.
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In this paper, we propose a new unsupervised feature selection algorithm by considering the nonlinear and similarity relationships within the data. To achieve this, we apply the kernel method and local structure learning to consider the nonlinear relationship between features and the local similarity between features. Specifically, we use a kernel function to map each feature of the data into the kernel space. In the high-dimensional kernel space, different features correspond to different weights, and zero weights are unimportant features (e.g. redundant features). Furthermore, we consider the similarity between features through local structure learning, and propose an effective optimization method to solve it. The experimental results show that the proposed algorithm achieves better performance than the comparison algorithm.
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Wang, Xin, Can Tang, Ji Li, Peng Zhang, and Wei Wang. "Image Target Recognition via Mixed Feature-Based Joint Sparse Representation." Computational Intelligence and Neuroscience 2020 (August 10, 2020): 1–8. http://dx.doi.org/10.1155/2020/8887453.
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An image target recognition approach based on mixed features and adaptive weighted joint sparse representation is proposed in this paper. This method is robust to the illumination variation, deformation, and rotation of the target image. It is a data-lightweight classification framework, which can recognize targets well with few training samples. First, Gabor wavelet transform and convolutional neural network (CNN) are used to extract the Gabor wavelet features and deep features of training samples and test samples, respectively. Then, the contribution weights of the Gabor wavelet feature vector and the deep feature vector are calculated. After adaptive weighted reconstruction, we can form the mixed features and obtain the training sample feature set and test sample feature set. Aiming at the high-dimensional problem of mixed features, we use principal component analysis (PCA) to reduce the dimensions. Lastly, the public features and private features of images are extracted from the training sample feature set so as to construct the joint feature dictionary. Based on joint feature dictionary, the sparse representation based classifier (SRC) is used to recognize the targets. The experiments on different datasets show that this approach is superior to some other advanced methods.
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Shen, Ning-Min, Jing Li, Pei-Yun Zhou, Ying Huo, and Yi Zhuang. "BSFCoS: Block and Sparse Principal Component Analysis-Based Fast Co-Saliency Detection Method." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 01 (December 30, 2015): 1655003. http://dx.doi.org/10.1142/s021800141655003x.
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Co-saliency detection, an emerging research area in saliency detection, aims to extract the common saliency from the multi images. The extracted co-saliency map has been utilized in various applications, such as in co-segmentation, co-recognition and so on. With the rapid development of image acquisition technology, the original digital images are becoming more and more clearly. The existing co-saliency detection methods processing these images need enormous computer memory along with high computational complexity. These limitations made it hard to satisfy the demand of real-time user interaction. This paper proposes a fast co-saliency detection method based on the image block partition and sparse feature extraction method (BSFCoS). Firstly, the images are divided into several uniform blocks, and the low-level features are extracted from Lab and RGB color spaces. In order to maintain the characteristics of the original images and reduce the number of feature points as well as possible, Truncated Power for sparse principal components method are employed to extract sparse features. Furthermore, K-Means method is adopted to cluster the extracted sparse features, and calculate the three salient feature weights. Finally, the co-saliency map was acquired from the feature fusion of the saliency map for single image and multi images. The proposed method has been tested and simulated on two benchmark datasets: Co-saliency Pairs and CMU Cornell iCoseg datasets. Compared with the existing co-saliency methods, BSFCoS has a significant running time improvement in multi images processing while ensuring detection results. Lastly, the co-segmentation method based on BSFCoS is also given and has a better co-segmentation performance.
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Li, Ning, Weiping Tu, and Haojun Ai. "A Sparse Feature Matching Model Using a Transformer towards Large-View Indoor Visual Localization." Wireless Communications and Mobile Computing 2022 (July 4, 2022): 1–12. http://dx.doi.org/10.1155/2022/1243041.
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Accurate indoor visual localization has been a challenging task under large-view scenes with wide baselines and weak texture images, where it is difficult to accomplish accurate image matching. To address the problem of sparse image features mismatching, we develop a coarse-to-fine feature matching model using a transformer, termed MSFA-T, which assigns the corresponding semantic labels to image features for an incipient coarse matching. To avoid the anomalous scoring of sparse feature interrelationship in the attention assigning phase, we propose a multiscale forward attention mechanism that decomposes the similarity-based features to learn the specificity of sparse features, the influence of position-independence on sparse features is reduced and the performance of the fine image matching in visual localization is effectively improved. We conduct extensive experiments on the challenging datasets; the results show that our model achieves image matching with an average 79.8% probability of the area under the cumulative curve of the corner point error, which outperforms the related state-of-the-art algorithms by an improvement of 13% probability at 1 m accuracy for the image-based visual localization in large view scenes.
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Zang, Mujun, Dunwei Wen, Tong Liu, Hailin Zou, and Chanjuan Liu. "A Fast Sparse Coding Method for Image Classification." Applied Sciences 9, no. 3 (February 1, 2019): 505. http://dx.doi.org/10.3390/app9030505.
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Image classification is an important problem in computer vision. The sparse coding spatial pyramid matching (ScSPM) framework is widely used in this field. However, the sparse coding cannot effectively handle very large training sets because of its high computational complexity, and ignoring the mutual dependence among local features results in highly variable sparse codes even for similar features. To overcome the shortcomings of previous sparse coding algorithm, we present an image classification method, which replaces the sparse dictionary with a stable dictionary learned via low computational complexity clustering, more specifically, a k-medoids cluster method optimized by k-means++. The proposed method can reduce the learning complexity and improve the feature’s stability. In the experiments, we compared the effectiveness of our method with the existing ScSPM method and its improved versions. We evaluated our approach on two diverse datasets: Caltech-101 and UIUC-Sports. The results show that our method can increase the accuracy of spatial pyramid matching, which suggests that our method is capable of improving performance of sparse coding features.
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Zhao, Yue, and Jianbo Su. "New Sparse Facial Feature Description Model Based on Salience Evaluation of Regions and Features." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 05 (July 9, 2015): 1556007. http://dx.doi.org/10.1142/s0218001415560078.
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Some regions (or blocks) and their affiliated features of face images are normally of more importance for face recognition. However, the variety of feature contributions, which exerts different saliency on recognition, is usually ignored. This paper proposes a new sparse facial feature description model based on salience evaluation of regions and features, which not only considers the contributions of different face regions, but also distinguishes that of different features in the same region. Specifically, the structured sparse learning scheme is employed as the salience evaluation method to encourage sparsity at both the group and individual levels for balancing regions and features. Therefore, the new facial feature description model is obtained by combining the salience evaluation method with region-based features. Experimental results show that the proposed model achieves better performance with much lower feature dimensionality.
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Zhou, Junxiu, Yangyang Tao, and Xian Liu. "Tensor Decomposition for Salient Object Detection in Images." Big Data and Cognitive Computing 3, no. 2 (June 19, 2019): 33. http://dx.doi.org/10.3390/bdcc3020033.
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The fundamental challenge of salient object detection is to find the decision boundary that separates the salient object from the background. Low-rank recovery models address this challenge by decomposing an image or image feature-based matrix into a low-rank matrix representing the image background and a sparse matrix representing salient objects. This method is simple and efficient in finding salient objects. However, it needs to convert high-dimensional feature space into a two-dimensional matrix. Therefore, it does not take full advantage of image features in discovering the salient object. In this article, we propose a tensor decomposition method which considers spatial consistency and tries to make full use of image feature information in detecting salient objects. First, we use high-dimensional image features in tensor to preserve spatial information about image features. Following this, we use a tensor low-rank and sparse model to decompose the image feature tensor into a low-rank tensor and a sparse tensor, where the low-rank tensor represents the background and the sparse tensor is used to identify the salient object. To solve the tensor low-rank and sparse model, we employed a heuristic strategy by relaxing the definition of tensor trace norm and tensor l1-norm. Experimental results on three saliency benchmarks demonstrate the effectiveness of the proposed tensor decomposition method.
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Harris, Chelsea, Uchenna Okorie, and Sokratis Makrogiannis. "Spatially localized sparse approximations of deep features for breast mass characterization." Mathematical Biosciences and Engineering 20, no. 9 (2023): 15859–82. http://dx.doi.org/10.3934/mbe.2023706.
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<abstract><p>We propose a deep feature-based sparse approximation classification technique for classification of breast masses into benign and malignant categories in film screen mammographs. This is a significant application as breast cancer is a leading cause of death in the modern world and improvements in diagnosis may help to decrease rates of mortality for large populations. While deep learning techniques have produced remarkable results in the field of computer-aided diagnosis of breast cancer, there are several aspects of this field that remain under-studied. In this work, we investigate the applicability of deep-feature-generated dictionaries to sparse approximation-based classification. To this end we construct dictionaries from deep features and compute sparse approximations of Regions Of Interest (ROIs) of breast masses for classification. Furthermore, we propose block and patch decomposition methods to construct overcomplete dictionaries suitable for sparse coding. The effectiveness of our deep feature spatially localized ensemble sparse analysis (DF-SLESA) technique is evaluated on a merged dataset of mass ROIs from the CBIS-DDSM and MIAS datasets. Experimental results indicate that dictionaries of deep features yield more discriminative sparse approximations of mass characteristics than dictionaries of imaging patterns and dictionaries learned by unsupervised machine learning techniques such as K-SVD. Of note is that the proposed block and patch decomposition strategies may help to simplify the sparse coding problem and to find tractable solutions. The proposed technique achieves competitive performances with state-of-the-art techniques for benign/malignant breast mass classification, using 10-fold cross-validation in merged datasets of film screen mammograms.</p></abstract>
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Liang, Lin, Xingyun Ding, Fei Liu, Yuanming Chen, and Haobin Wen. "Feature Extraction Using Sparse Kernel Non-Negative Matrix Factorization for Rolling Element Bearing Diagnosis." Sensors 21, no. 11 (May 25, 2021): 3680. http://dx.doi.org/10.3390/s21113680.
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For early fault detection of a bearing, the localized defect generally brings a complex vibration signal, so it is difficult to detect the periodic transient characteristics from the signal spectrum using conventional bearing fault diagnosis methods. Therefore, many matrix analysis technologies, such as singular value decomposition (SVD) and reweighted SVD (RSVD), were proposed recently to solve this problem. However, such technologies also face failure in bearing fault detection due to the poor interpretability of the obtained eigenvector. Non-negative Matrix Factorization (NMF), as a part-based representation algorithm, can extract low-rank basis spaces with natural sparsity from the time–frequency representation. It performs excellent interpretability of the factor matrices due to its non-negative constraints. By this virtue, NMF can extract the fault feature by separating the frequency bands of resonance regions from the amplitude spectrogram automatically. In this paper, a new feature extraction method based on sparse kernel NMF (KNMF) was proposed to extract the fault features from the amplitude spectrogram in greater depth. By decomposing the amplitude spectrogram using the kernel-based NMF model with L1 regularization, sparser spectral bases can be obtained. Using KNMF with the linear kernel function, the time–frequency distribution of the vibration signal can be decomposed into a subspace with different frequency bands. Thus, we can extract the fault features, a series of periodic impulses, from the decomposed subspace according to the sparse frequency bands in the spectral bases. As a result, the proposed method shows a very high performance in extracting fault features, which is verified by experimental investigations and benchmarked by the Fast Kurtogram, SVD and NMF-based methods.
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Wang, Longhao, Chaozhen Lan, Beibei Wu, Tian Gao, Zijun Wei, and Fushan Yao. "A Method for Detecting Feature-Sparse Regions and Matching Enhancement." Remote Sensing 14, no. 24 (December 8, 2022): 6214. http://dx.doi.org/10.3390/rs14246214.
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Image matching is a key research issue in the intelligent processing of remote sensing images. Due to the large phase differences or apparent differences in ground features between unmanned aerial vehicle imagery and satellite imagery, as well as the large number of sparsely textured areas, image matching between the two types of imagery is very difficult. Tackling the difficult problem of matching unmanned aerial vehicle imagery and satellite imagery, a feature sparse region detection and matching enhancement algorithm (SD-ME) is proposed in this study. First, the SuperGlue algorithm was used to initially match the two images, and feature-sparse region detection was performed with the help of the image features and initial matching results, with the detected feature sparse areas stored in a linked list one by one. Then, according to the order of storage, feature re-extraction was performed on the feature-sparse areas individually, and an adaptive threshold feature screening algorithm was proposed to filter and screen the re-extracted features. This retains only high-confidence features in the region and improves the reliability of matching enhancement results. Finally, local features with high scores that were re-extracted in the feature-sparse areas were aggregated and input to the SuperGlue network for matching, and thus, reliable matching enhancement results were obtained. The experiment selected four pairs of un-manned aerial vehicle imagery and satellite imagery that were difficult to match and compared the SD-ME algorithm with the SIFT, ContextDesc, and SuperGlue algorithms. The results revealed that the SD-ME algorithm was far superior to other algorithms in terms of the number of correct matching points, the accuracy of matching points, and the uniformity of distribution of matching points. The number of correctly matched points in each image pair increased by an average of 95.52% compared to SuperGlue. The SD-ME algorithm can effectively improve the matching quality between unmanned aerial vehicle imagery and satellite imagery and has practical value in the fields of image registration and change detection.
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Nan Dong, Fuqiang Liu, and Zhipeng Li. "Crowd Density Estimation Using Sparse Texture Features." Journal of Convergence Information Technology 5, no. 6 (August 31, 2010): 125–37. http://dx.doi.org/10.4156/jcit.vol5.issue6.13.
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Li, B., Q. Meng, and H. Holstein. "Articulated Pose Identification With Sparse Point Features." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 3 (June 2004): 1412–22. http://dx.doi.org/10.1109/tsmcb.2004.825914.
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Han, H., and X. J. Li. "Human action recognition with sparse geometric features." Imaging Science Journal 63, no. 1 (October 14, 2014): 45–53. http://dx.doi.org/10.1179/1743131x14y.0000000091.
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Zhou, Hongdi, Lin Zhu, and Xixing Li. "Fault Diagnosis Method for Rolling Bearing Based on Sparse Principal Subspace Discriminant Analysis." Shock and Vibration 2022 (April 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/8946094.
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Rolling bearings are omnipresent parts in industrial fields. To comprehensively reflect the status of rolling bearing and improve the classification accuracy, fusion information is widely used in various studies, which may result in high dimensionality, redundancy information of dataset, and time consumption. Thus, it is of crucial significance in extracting optimal features from high-dimensional and redundant feature space for classification. In this study, a fault diagnosis of rolling bearings model based on sparse principal subspace discriminant analysis is proposed. It extracts sparse discrimination information, meanwhile preserving the main energy of original dataset, and the sparse regularization term and sparse error term constrained by l2,1-norm are introduced to improve the performance of feature extraction and the robustness to noise and outliers. The multi-domain feature space involved a time domain, frequency domain, and time-frequency domain is first derived from the original vibration signals. Then, the intrinsic geometric features extracted by sparse principal subspace discriminant analysis are fed into a support vector machine classifier to recognize different operating conditions of bearings. The experimental results demonstrated that the feasibility and effectiveness of the proposed fault diagnosis model based on a sparse principal subspace discriminant analysis algorithm can achieve higher recognition accuracy than fisher discriminant analysis and its extensions, and it is relatively insensitive to the impact of noise and outliers owing to the sparse property.
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Wersing, Heiko, and Edgar Körner. "Learning Optimized Features for Hierarchical Models of Invariant Object Recognition." Neural Computation 15, no. 7 (July 1, 2003): 1559–88. http://dx.doi.org/10.1162/089976603321891800.
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There is an ongoing debate over the capabilities of hierarchical neural feedforward architectures for performing real-world invariant object recognition. Although a variety of hierarchical models exists, appropriate supervised and unsupervised learning methods are still an issue of intense research. We propose a feedforward model for recognition that shares components like weight sharing, pooling stages, and competitive nonlinearities with earlier approaches but focuses on new methods for learning optimal feature-detecting cells in intermediate stages of the hierarchical network. We show that principles of sparse coding, which were previously mostly applied to the initial feature detection stages, can also be employed to obtain optimized intermediate complex features. We suggest a new approach to optimize the learning of sparse features under the constraints of a weight-sharing or convolutional architecture that uses pooling operations to achieve gradual invariance in the feature hierarchy. The approach explicitly enforces symmetry constraints like translation invariance on the feature set. This leads to a dimension reduction in the search space of optimal features and allows determining more efficiently the basis representatives, which achieve a sparse decomposition of the input. We analyze the quality of the learned feature representation by investigating the recognition performance of the resulting hierarchical network on object and face databases. We show that a hierarchy with features learned on a single object data set can also be applied to face recognition without parameter changes and is competitive with other recent machine learning recognition approaches. To investigate the effect of the interplay between sparse coding and processing nonlinearities, we also consider alternative feedforward pooling nonlinearities such as presynaptic maximum selection and sum-of-squares integration. The comparison shows that a combination of strong competitive nonlinearities with sparse coding offers the best recognition performance in the difficult scenario of segmentation-free recognition in cluttered surround. We demonstrate that for both learning and recognition, a precise segmentation of the objects is not necessary.
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Wang, HongChao, and WenLiao Du. "Intelligent diagnosis of rolling bearing compound faults based on device state dictionary set sparse decomposition feature extraction–hidden Markov model." Advances in Mechanical Engineering 12, no. 6 (June 2020): 168781402093046. http://dx.doi.org/10.1177/1687814020930469.
Full textAbstract:
Identification of rolling bearing fault patterns, especially for the compound faults, has attracted notable attention and is still a challenge in fault diagnosis. Intelligent diagnosis method is an effective method for compound faults of rolling element bearing, and effective fault feature extraction is the key step to decide the intelligent diagnosis result to some extent. The sparse decomposition method could capture the complex impulsive characteristic components of rolling bearing more effectively than the other time–frequency analysis method when compound fault arises in rolling bearing. Based on the self-learning dictionary under different operating states of the device corresponding to the special features modes, an intelligent diagnosis method of rolling bearing compound faults based on device state dictionary set sparse decomposition feature extraction–hidden Markov model is proposed in the article. First, characteristic dictionaries of rolling bearing under different operating conditions are extracted by sparse decomposition self-learning method, and state dictionary set of rolling bearing is constructed. Then, the compound fault signals of bearing are transformed into sparse domain using the constructed dictionary set to extract sparse features. At last, the extracted sparse features are used as training and testing vectors of hidden Markov model, and satisfactory intelligent diagnosis results are obtained. The validity of the proposed method is verified by compound faults of rolling element bearing. In addition, the advantages of the proposed method are also verified by comparing with the other feature extraction and intelligent diagnosis methods, and the proposed method provides a feasible and efficient solution for fault diagnosis of rolling bearing compound faults.
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Nardone, Davide, Angelo Ciaramella, and Antonino Staiano. "A Sparse-Modeling Based Approach for Class Specific Feature Selection." PeerJ Computer Science 5 (November 18, 2019): e237. http://dx.doi.org/10.7717/peerj-cs.237.
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In this work, we propose a novel Feature Selection framework called Sparse-Modeling Based Approach for Class Specific Feature Selection (SMBA-CSFS), that simultaneously exploits the idea of Sparse Modeling and Class-Specific Feature Selection. Feature selection plays a key role in several fields (e.g., computational biology), making it possible to treat models with fewer variables which, in turn, are easier to explain, by providing valuable insights on the importance of their role, and likely speeding up the experimental validation. Unfortunately, also corroborated by the no free lunch theorems, none of the approaches in literature is the most apt to detect the optimal feature subset for building a final model, thus it still represents a challenge. The proposed feature selection procedure conceives a two-step approach: (a) a sparse modeling-based learning technique is first used to find the best subset of features, for each class of a training set; (b) the discovered feature subsets are then fed to a class-specific feature selection scheme, in order to assess the effectiveness of the selected features in classification tasks. To this end, an ensemble of classifiers is built, where each classifier is trained on its own feature subset discovered in the previous phase, and a proper decision rule is adopted to compute the ensemble responses. In order to evaluate the performance of the proposed method, extensive experiments have been performed on publicly available datasets, in particular belonging to the computational biology field where feature selection is indispensable: the acute lymphoblastic leukemia and acute myeloid leukemia, the human carcinomas, the human lung carcinomas, the diffuse large B-cell lymphoma, and the malignant glioma. SMBA-CSFS is able to identify/retrieve the most representative features that maximize the classification accuracy. With top 20 and 80 features, SMBA-CSFS exhibits a promising performance when compared to its competitors from literature, on all considered datasets, especially those with a higher number of features. Experiments show that the proposed approach may outperform the state-of-the-art methods when the number of features is high. For this reason, the introduced approach proposes itself for selection and classification of data with a large number of features and classes.
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Peng, Wei, Dong Wang, Changqing Shen, and Dongni Liu. "Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features." Shock and Vibration 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/1835127.
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Sparse signal representations attract much attention in the community of signal processing because only a few coefficients are required to represent a signal and these coefficients make the signal understandable. For bearing faults’ diagnosis, bearing faults signals collected from transducers are often overwhelmed by strong low-frequency periodic signals and heavy noises. In this paper, a joint signal processing method is proposed to extract sparse envelope coefficients, which are the sparse signal representations of bearing fault signals. Firstly, to enhance bearing fault signals, particle swarm optimization is introduced to tune the parameters of wavelet transform and the optimal wavelet transform is used for retaining one of the resonant frequency bands. Thus, sparse wavelet coefficients are obtained. Secondly, to reduce the in-band noises existing in the sparse wavelet coefficients, an adaptive morphological analysis with an iterative local maximum detection method is developed to extract sparse envelope coefficients. Simulated and real bearing fault signals are investigated to illustrate how the sparse envelope coefficients are extracted. The results show that the sparse envelope coefficients can be used to represent bearing fault features and identify different localized bearing faults.
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Zhang, Zongzhen, Shunming Li, Zenghui An, and Yu Xin. "Fast convolution sparse filtering and its application on gearbox fault diagnosis." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 9 (April 6, 2020): 2291–304. http://dx.doi.org/10.1177/0954407020907818.
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Transmission, as a critical part of vehicles, is the hub of power transmission and the core of controlling speed change. Condition monitoring and diagnosis of transmissions have become an effective tool to ensure vehicle safety travelling. The intelligent fault diagnosis strategy using artificial intelligent methods has been studied and applied for gearbox fault diagnosis. However, most algorithms cannot guarantee both accuracy and training efficiency. In this paper, fast convolutional sparse filtering based on convolutional activation and feature normalization is proposed for gearbox fault diagnosis without any time-consuming preprocessing. In fast convolutional sparse filtering, the features of samples are optimized instead of local features, which could obviously reduce the dimension and construction time of the Hessian matrix. In addition, the output features are equally active to guarantee that all features have similar contributions. The l2-norm of the training features is recorded and used for pseudo-normalization of the test features. The proposed fast convolutional sparse filtering is validated by a bearing fault dataset and a planetary gear fault dataset. Verification results confirm that fast convolutional sparse filtering is a promising tool for fault diagnosis, which has obviously improved the diagnosis accuracy, training efficiency, and robustness and provides the greater advantage of handling large-scale datasets.
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Xia, Shiqi, Yimin Xia, and Jiawei Xiang. "Piston Wear Detection and Feature Selection Based on Vibration Signals Using the Improved Spare Support Vector Machine for Axial Piston Pumps." Materials 15, no. 23 (November 29, 2022): 8504. http://dx.doi.org/10.3390/ma15238504.
Full textAbstract:
A piston wear fault is a major failure mode of axial piston pumps, which may decrease their volumetric efficiency and service life. Although fault detection based on machine learning theory can achieve high accuracy, the performance mainly depends on the detection model and feature selection. Feature selection in learning has recently emerged as a crucial issue. Therefore, piston wear detection and feature selection are essential and urgent. In this paper, we propose a vibration signal-based methodology using the improved spare support vector machine, which can integrate the feature selection into the piston wear detection learning process. Forty features are defined to capture the piston wear signature in the time domain, frequency domain, and time–frequency domain. The relevance and impact of sparsity in 40 features are illustrated through the single and multiple statistical feature analysis. Model performance is assessed and the sparse features are discovered. The maximum model testing and training accuracy are 97.50% and 96.60%, respectively. Spare features s10, s12, Ew(8), x7, Ee(5), and Ee(4) are selected and validated. Results show that the proposed methodology is applicable for piston wear detection and feature selection, with high model accuracy and good feature sparsity.
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Afshar, Majid, and Hamid Usefi. "Optimizing feature selection methods by removing irrelevant features using sparse least squares." Expert Systems with Applications 200 (August 2022): 116928. http://dx.doi.org/10.1016/j.eswa.2022.116928.
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Dai, Ling, Guangyun Zhang, Jinqi Gong, and Rongting Zhang. "Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data." Applied Sciences 11, no. 21 (November 8, 2021): 10502. http://dx.doi.org/10.3390/app112110502.
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In the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all areas. In order to not rely on expert knowledge and find the effective feature index with regard to a certain material automatically, this paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. The key point explicitly expresses the interaction relationship between original features as new features by multiplication or division operation in the logistic regression. Through the strong constraint of the L1 norm, the learned features are sparse. The coefficient value of the corresponding features after sparse represents the basis for judging the importance of the features, and the optimal interactive features among the original features. This expression is inspired by the phenomenon that usually the famous indexes we used in remote sensing, like NDVI, NDWI, are the ratio between different spectral bands, and also in statistical regression, the relationship between features is captured by feature value multiplication. Experiments were conducted on three hyperspectral data sets of Pavia Center, Washington DC Mall, and Pavia University. The results for binary classification show that the method can extract the NDVI and NDWI autonomously, and a new type of metal index is proposed in the Pavia University data set. This framework is more flexible and creative than the traditional method based on laboratory research to obtain the key feature and feature interaction index for hyperspectral remotely sensed data.
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Yan, Jingjie, Xiaolan Wang, Weiyi Gu, and LiLi Ma. "Speech Emotion Recognition Based on Sparse Representation." Archives of Acoustics 38, no. 4 (December 1, 2013): 465–70. http://dx.doi.org/10.2478/aoa-2013-0055.
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Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.
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Henry, Rawn, Olivia Hsu, Rohan Yadav, Stephen Chou, Kunle Olukotun, Saman Amarasinghe, and Fredrik Kjolstad. "Compilation of sparse array programming models." Proceedings of the ACM on Programming Languages 5, OOPSLA (October 20, 2021): 1–29. http://dx.doi.org/10.1145/3485505.
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This paper shows how to compile sparse array programming languages. A sparse array programming language is an array programming language that supports element-wise application, reduction, and broadcasting of arbitrary functions over dense and sparse arrays with any fill value. Such a language has great expressive power and can express sparse and dense linear and tensor algebra, functions over images, exclusion and inclusion filters, and even graph algorithms. Our compiler strategy generalizes prior work in the literature on sparse tensor algebra compilation to support any function applied to sparse arrays, instead of only addition and multiplication. To achieve this, we generalize the notion of sparse iteration spaces beyond intersections and unions. These iteration spaces are automatically derived by considering how algebraic properties annotated onto functions interact with the fill values of the arrays. We then show how to compile these iteration spaces to efficient code. When compared with two widely-used Python sparse array packages, our evaluation shows that we generate built-in sparse array library features with a performance of 1.4× to 53.7× when measured against PyData/Sparse for user-defined functions and between 0.98× and 5.53× when measured against SciPy/Sparse for sparse array slicing. Our technique outperforms PyData/Sparse by 6.58× to 70.3×, and (where applicable) performs between 0.96× and 28.9× that of a dense NumPy implementation, on end-to-end sparse array applications. We also implement graph linear algebra kernels in our system with a performance of between 0.56× and 3.50× compared to that of the hand-optimized SuiteSparse:GraphBLAS library.
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Guo, Xiyang. "Research on Mushroom Image Classification Algorithm Based on Deep Sparse Dictionary Learning." Academic Journal of Science and Technology 9, no. 1 (January 20, 2024): 235–40. http://dx.doi.org/10.54097/1f3xnx82.
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The traditional mushroom feature extraction method has low classification efficiency and unsatisfactory effect. Dictionary learning is widely used in image classification. However, the previous work is to learn dictionaries in the original space, which limits the performance of sparse representation classification. In order to solve the problem of spatial redundancy in traditional convolutional neural networks and the weak performance of deep learning in small samples, an improved dictionary learning algorithm, Deep Sparse Dictionary learning (DSDL), is proposed. The input to DSDL is not a matrix gathered from the original grayscale image or a hand-created feature, but rather a relatively deeper feature extraction via a stack autoencoder. Then, a structured dictionary is designed to reconstruct the deep features according to different categories of distinguishing features. In addition, it is necessary to learn the associated structured projection sparse dictionary to ensure that the decoder updates in the direction of the deconvolution operator error is minimal. By utilizing sparse dictionary learning loss functions and autoencoder loss functions, DSDL can simultaneously learn deep latent features and corresponding dictionary pairs. In the testing phase of DSDL, the minimum errors of deep feature and structured projection components for different classes can be directly represented by basic matrix multiplication operations. Experimental results show that the proposed method achieves a good classification effect on mushroom images, which shows the effectiveness of the method.
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He, Jiahui, Zhijun Cheng, and Bo Guo. "Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based Method." Sensors 22, no. 17 (August 24, 2022): 6358. http://dx.doi.org/10.3390/s22176358.
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Anomaly detection based on telemetry data is a major issue in satellite health monitoring which can identify unusual or unexpected events, helping to avoid serious accidents and ensure the safety and reliability of operations. In recent years, sparse representation techniques have received an increasing amount of interest in anomaly detection, although its applications in satellites are still being explored. In this paper, a novel sparse feature-based anomaly detection method (SFAD) is proposed to identify hybrid anomalies in telemetry. First, a telemetry data dictionary and the corresponding sparse matrix are obtained through K-means Singular Value Decomposition (K-SVD) algorithms, then sparse features are defined from the sparse matrix containing the local dynamics and co-occurrence relations in the multivariate telemetry time series. Finally, lower-dimensional sparse features vectors are input to a one-class support vector machine (OCSVM) to detect anomalies in telemetry. Case analysis based on satellite antenna telemetry data shows that the detection precision, F1-score and FPR of the proposed method are improved compared with other existing multivariate anomaly detection methods, illustrating the good effectiveness of this method.
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Xidao, Luan, Xie Yuxiang, Zhang Lili, Zhang Xin, Li Chen, and He Jingmeng. "An Image Similarity Acceleration Detection Algorithm Based on Sparse Coding." Mathematical Problems in Engineering 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/1917421.
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Aiming at the problem that the image similarity detection efficiency is low based on local feature, an algorithm called ScSIFT for image similarity acceleration detection based on sparse coding is proposed. The algorithm improves the image similarity matching speed by sparse coding and indexing the extracted local features. Firstly, the SIFT feature of the image is extracted as a training sample to complete the overcomplete dictionary, and a set of overcomplete bases is obtained. The SIFT feature vector of the image is sparse-coded with the overcomplete dictionary, and the sparse feature vector is used to build an index. The image similarity detection result is obtained by comparing the sparse coefficients. The experimental results show that the proposed algorithm can significantly improve the detection speed compared with the traditional algorithm based on local feature detection under the premise of guaranteeing the accuracy of algorithm detection.
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Chen, Zhong, Shengwu Xiong, Zhixiang Fang, Ruiling Zhang, Xiangzhen Kong, and Yi Rong. "Topologically Ordered Feature Extraction Based on Sparse Group Restricted Boltzmann Machines." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/267478.
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How to extract topologically ordered features efficiently from high-dimensional data is an important problem of unsupervised feature learning domains for deep learning. To address this problem, we propose a new type of regularization for Restricted Boltzmann Machines (RBMs). Adding two extra terms in the log-likelihood function to penalize the group weights and topologically ordered factors, this type of regularization extracts topologically ordered features based on sparse group Restricted Boltzmann Machines (SGRBMs). Therefore, it encourages an RBM to learn a much smoother probability distribution because its formulations turn out to be a combination of the group weight-decay and topologically ordered factor regularizations. We apply this proposed regularization scheme to image datasets of natural images and Flying Apsara images in the Dunhuang Grotto Murals at four different historical periods. The experimental results demonstrate that the combination of these two extra terms in the log-likelihood function helps to extract more discriminative features with much sparser and more aggregative hidden activation probabilities.
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Zhang, Yayu, Yuhua Qian, Guoshuai Ma, Keyin Zheng, Guoqing Liu, and Qingfu Zhang. "Learning Multi-Task Sparse Representation Based on Fisher Information." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (March 24, 2024): 16899–907. http://dx.doi.org/10.1609/aaai.v38i15.29632.
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Multi-task learning deals with multiple related tasks simultaneously by sharing knowledge. In a typical deep multi-task learning model, all tasks use the same feature space and share the latent knowledge. If the tasks are weakly correlated or some features are negatively correlated, sharing all knowledge often leads to negative knowledge transfer among. To overcome this issue, this paper proposes a Fisher sparse multi-task learning method. It can obtain a sparse sharing representation for each task. In such a way, tasks share features on a sparse subspace. Our method can ensure that the knowledge transferred among tasks is beneficial. Specifically, we first propose a sparse deep multi-task learning model, and then introduce Fisher sparse module into traditional deep multi-task learning to learn the sparse variables of task. By alternately updating the neural network parameters and sparse variables, a sparse sharing representation can be learned for each task. In addition, in order to reduce the computational overhead, an heuristic method is used to estimate the Fisher information of neural network parameters. Experimental results show that, comparing with other methods, our proposed method can improve the performance for all tasks, and has high sparsity in multi-task learning.
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Wang, Bin, Yu Liu, Wei Wang, Wei Xu, and Mao Jun Zhang. "Local Spatiotemporal Coding and Sparse Representation Based Human Action Recognition." Applied Mechanics and Materials 401-403 (September 2013): 1555–60. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1555.
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To handle with the limitation of bag-of-features (BoF) model which ignores spatial and temporal relationships of local features in human action recognition in video, a Local Spatiotemporal Coding (LSC) is proposed. Rather than the exiting methods only uses the feature appearance information for coding, LSC encodes feature appearance and spatiotemporal positions information simultaneously with vector quantization (VQ). It can directly models the spatiotemporal relationships of local features in space time volume (STV). In implement, the local features are projected into sub-space-time-volume (sub-STV), and encoded with LSC. In addition a multi-level LSC is also provided. Then a group of sub-STV descriptors obtained from videos with multi-level LSC and Avg-pooling are used for action video classification. A sparse representation based classification method is adopted to classify action videos upon these sub-STV descriptors. The experimental results on KTH, Weizmann, and UCF sports datasets show that our method achieves better performance than the previous local spatiotemporal features based human action recognition methods.
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Kim, Hyuncheol, and Joonki Paik. "Low-Rank Representation-Based Object Tracking Using Multitask Feature Learning with Joint Sparsity." Abstract and Applied Analysis 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/147353.
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We address object tracking problem as a multitask feature learning process based on low-rank representation of features with joint sparsity. We first select features with low-rank representation within a number of initial frames to obtain subspace basis. Next, the features represented by the low-rank and sparse property are learned using a modified joint sparsity-based multitask feature learning framework. Both the features and sparse errors are then optimally updated using a novel incremental alternating direction method. The low-rank minimization problem for learning multitask features can be achieved by a few sequences of efficient closed form update process. Since the proposed method attempts to perform the feature learning problem in both multitask and low-rank manner, it can not only reduce the dimension but also improve the tracking performance without drift. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art tracking methods for tracking objects in challenging image sequences.
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Jin, Ju Bo, and Yu Xi Liu. "Sparse Representation of the Human Vision Information and the Saliency Detection Algorithm." Applied Mechanics and Materials 513-517 (February 2014): 3349–53. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3349.
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Representation and measurement are two important issues for saliency models. Different with previous works that learnt sparse features from large scale natural statistics, we propose to learn features from short-term statistics of single images. For saliency measurement, we defined basic firing rate (BFR) for each sparse feature, and then we propose to use feature activity rate (FAR) to measure the bottom-up visual saliency. The proposed FAR measure is biological plausible and easy to compute and with satisfied performance. Experiments on human trajectory positioning and psychological patterns demonstrate the effectiveness and robustness of our proposed method.
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Wang, Qing Wei, Zi Lu Ying, and Lian Wen Huang. "Face Recognition Algorithm Based on Haar-Like Features and Gentle Adaboost Feature Selection via Sparse Representation." Applied Mechanics and Materials 742 (March 2015): 299–302. http://dx.doi.org/10.4028/www.scientific.net/amm.742.299.
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This paper proposed a new face recognition algorithm based on Haar-Like features and Gentle Adaboost feature selection via sparse representation. Firstly, All the images including face images and non face images are normalized to size and then Haar-Like features are extracted . The number of Haar-Like features can be as large as 12,519. In order to reduce the feature dimension and retain the most effective features for face recognition, Gentle Adaboost algorithm is used for feature selection. Selected features are used for face recognition via sparse representation classification (SRC) algorithm. Testing experiments were carried out on the AR database to test the performance of the new proposed algorithm. Compared with traditional algorithms like NS, NN, SRC, and SVM, the new algorithm achieved a better recognition rate. The effect of face recognition rate changing with feature dimension showed that the new proposed algorithm performed a higher recognition rate than SRC algorithm all the time with the increasing of feature dimension, which fully proved the effectiveness and superiority of the new proposed algorithm.
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Wang, Yong. "Online electronic signature recognition using sparse classification techniques that support neural models." Journal of Computational Methods in Sciences and Engineering 24, no. 1 (March 14, 2024): 263–75. http://dx.doi.org/10.3233/jcm-237025.
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With the rapid development of information technology, electronic signature plays an increasingly important role in people’s production practice. However, there are a large number of hackers maliciously stealing information in the network. In order to avoid this phenomenon, we urgently need to strengthen the research on online electronic signature recognition technology. Based on the sparse classification technology of neural model, this paper constructs an online electronic signature recognition model by using convolutional neural network and sparse classification technology. We first extract the local features of online electronic signatures, construct feature vectors and perform sparse representation. Sub-model we construct a scheme for online electronic signature recognition based on neural models and sparse classification techniques using a combination of algorithms. We first extract the local features of online electronic signatures, construct feature vectors and perform sparse representation. At the same time, the features in the training image set are extracted, local feature sets are constructed, feature dictionaries are created, and the vectors in the feature dictionaries are matched with the global sparse vectors constructed by the electronic signatures to be detected, and the matching results are finally obtained. At the same time, the features in the training image set are extracted, the local feature set is constructed, the feature dictionary is created, and the vector in the feature dictionary is matched with the global sparse vector constructed by the electronic signature to be detected, and finally the matching result is obtained. In order to verify the accuracy of the model, we first extracted 1000 respondents for online e-signature recognition experimental results show that the recognition accuracy of online e-signature has been significantly improved. Finally, in order to determine the optimal number of training sets for the model constructed in this experiment, we analyzed the correlation between training and sample size and recognition accuracy. Finally, it was concluded that the recognition accuracy increased with the increase of the number of training samples. Electronic signatures can quickly examine the signature results, and electronic signature recognition can be used to fix and tamper-proof evidence to enhance the security and trustworthiness of signatures, and it is imperative to improve the security of electronic signatures. In this paper, we study online electronic signature recognition technology, using neural model and sparse classification to construct an efficient and accurate recognition model. Experiments show that the model is effective and the number of training samples affects the recognition accuracy. This paper provides a new approach for the development of this technique. When the training samples are greater than 1300, the recognition accuracy is stable at 95%. This research has certain theoretical and practical significance, and promotes the rapid development of online electronic signature recognition.
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Yang, Honghui, and Shuzhen Yi. "Underwater Acoustic Target Feature Fusion Method Based on Multi-Kernel Sparsity Preserve Multi-Set Canonical Correlation Analysis." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 1 (February 2019): 87–92. http://dx.doi.org/10.1051/jnwpu/20193710087.
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To solve high-dimensional and small-sample-size classification problem for underwater target recognition, a new feature fusion method is proposed based on multi-kernel sparsity preserve multi-set canonical correlation analysis. The multi-set canonical correlation analysis algorithm is used to quantitatively analyze the correlation of multi-domain features, remove redundant and noise features, in order to achieve multi-domain feature fusion. The multi-kernel sparsely preserved projection algorithm is used to constrain the sparse reconstruction of the extracted multi-domain feature samples, which enhances the feature's classification ability. Results of applying real radiated noise datasets to underwater target recognition experiments show that our new method can effectively remove the redundancy and noise features, achieve the fusion of multi-domain underwater target features, and improve the recognition accuracy of underwater targets.
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Yuan, Ye, Jiang Chen, Hong Lang, and Jian (John) Lu. "Exploring the Efficacy of Sparse Feature in Pavement Distress Image Classification: A Focus on Pavement-Specific Knowledge." Applied Sciences 13, no. 18 (September 5, 2023): 9996. http://dx.doi.org/10.3390/app13189996.
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Road surface deterioration, such as cracks and potholes, poses a significant threat to both road safety and infrastructure longevity. Swift and accurate detection of these issues is crucial for timely maintenance and user security. However, current techniques often overlook the unique characteristics of pavement images, where the small distressed areas are vastly outnumbered by the background. In response, we propose an innovative road distress classification model that capitalizes on sparse perception. Our method introduces a sparse feature extraction module using dilated convolution, tailored to capture and combine sparse features of different scales from the image. To further enhance our model, we design a specialized loss function rooted in domain-specific knowledge about pavement distress. This loss function enforces sparsity during feature extraction, guiding the model to align precisely with the sparse distribution of target features. We validate the strength and effectiveness of our model through comprehensive evaluations of a diverse dataset of road images containing various distress types and conditions. Our approach exhibits significant potential in advancing traffic safety by enabling more efficient and accurate detection and classification of road distress.
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Hasler, Stephan, Heiko Wersing, and Edgar Körner. "Combining Reconstruction and Discrimination with Class-Specific Sparse Coding." Neural Computation 19, no. 7 (July 2007): 1897–918. http://dx.doi.org/10.1162/neco.2007.19.7.1897.
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Sparse coding is an important approach for the unsupervised learning of sensory features. In this contribution, we present two new methods that extend the traditional sparse coding approach with supervised components. Our goal is to increase the suitability of the learned features for classification tasks while keeping most of their general representation capability. We analyze the effect of the new methods using visualization on artificial data and discuss the results on two object test sets with regard to the properties of the found feature representation.