Bibliografías temáticas / Sparse features

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Literatura académica sobre el tema "Sparse features"

Autor: Grafiati
Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "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 (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
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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 (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
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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 (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 filte
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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. Aimi
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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 (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 s
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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 (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 prod
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Grimes, David B., and Rajesh P. N. Rao. "Bilinear Sparse Coding for Invariant Vision." Neural Computation 17, no. 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 mod
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Tesis sobre el tema "Sparse features"

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Strohmann, Thomas. "Very sparse kernel models: Predicting with few examples and few features." Diss., Connect to online resource, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3239405.

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Radwan, Noha [Verfasser], and Wolfram [Akademischer Betreuer] Burgard. "Leveraging sparse and dense features for reliable state estimation in urban environments." Freiburg : Universität, 2019. http://d-nb.info/1190031361/34.

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Hata, Alberto Yukinobu. "Road features detection and sparse map-based vehicle localization in urban environments." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-08062017-090428/.

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Localization is one of the fundamental components of autonomous vehicles by enabling tasks as overtaking, lane keeping and self-navigation. Urban canyons and bad weather interfere with the reception of GPS satellite signal which prohibits the exclusive use of such technology for vehicle localization in urban places. Alternatively, map-aided localization methods have been employed to enable position estimation without the dependence on GPS devices. In this solution, the vehicle position is given as the place that best matches the sensor measurement to the environment map. Before building the m
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Pundlik, Shrinivas J. "Motion segmentation from clustering of sparse point features using spatially constrained mixture models." Connect to this title online, 2009. http://etd.lib.clemson.edu/documents/1252937182/.

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Quadros, Alistair James. "Representing 3D shape in sparse range images for urban object classification." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/10515.

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This thesis develops techniques for interpreting 3D range images acquired in outdoor environments at a low resolution. It focuses on the task of robustly capturing the shapes that comprise objects, in order to classify them. With the recent development of 3D sensors such as the Velodyne, it is now possible to capture range images at video frame rates, allowing mobile robots to observe dynamic scenes in 3D. To classify objects in these scenes, features are extracted from the data, which allows different regions to be matched. However, range images acquired at this speed are of low resolution,
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Mairal, Julien. "Sparse coding for machine learning, image processing and computer vision." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2010. http://tel.archives-ouvertes.fr/tel-00595312.

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We study in this thesis a particular machine learning approach to represent signals that that consists of modelling data as linear combinations of a few elements from a learned dictionary. It can be viewed as an extension of the classical wavelet framework, whose goal is to design such dictionaries (often orthonormal basis) that are adapted to natural signals. An important success of dictionary learning methods has been their ability to model natural image patches and the performance of image denoising algorithms that it has yielded. We address several open questions related to this framework:
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Abbasnejad, Iman. "Learning spatio-temporal features for efficient event detection." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/121184/1/Iman_Abbasnejad_Thesis.pdf.

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This thesis has addressed the topic of event detection in videos, which is a challenging problem as events to be detected, can be complex, correlated, and may require the detection of different objects and human actions. To address these challenges, the thesis has developed effective strategies for learning the spatio-temporal features of events. Improved event detection performance has been demonstrated on several real-world challenging databases. The outcome of our research will be useful for a number of applications including human computer interaction, robotics and video surveillance.
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Lakemond, Ruan. "Multiple camera management using wide baseline matching." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/37668/1/Ruan_Lakemond_Thesis.pdf.

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Camera calibration information is required in order for multiple camera networks to deliver more than the sum of many single camera systems. Methods exist for manually calibrating cameras with high accuracy. Manually calibrating networks with many cameras is, however, time consuming, expensive and impractical for networks that undergo frequent change. For this reason, automatic calibration techniques have been vigorously researched in recent years. Fully automatic calibration methods depend on the ability to automatically find point correspondences between overlapping views. In typical camera
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Umakanthan, Sabanadesan. "Human action recognition from video sequences." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/93749/1/Sabanadesan_Umakanthan_Thesis.pdf.

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This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of a
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Dhanjal, Charanpal. "Sparse Kernel feature extraction." Thesis, University of Southampton, 2008. https://eprints.soton.ac.uk/64875/.

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The presence of irrelevant features in training data is a significant obstacle for many machine learning tasks, since it can decrease accuracy, make it harder to understand the learned model and increase computational and memory requirements. One approach to this problem is to extract appropriate features. General approaches such as Principal Components Analysis (PCA) are successful for a variety of applications, however they can be improved upon by targeting feature extraction towards more specific problems. More recent work has been more focused and considers sparser formulations which poten
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Libros sobre el tema "Sparse features"

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Flexible Sparse Learning of Feature Subspaces. [publisher not identified], 2017.

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DiPietro, Vincent. Unusual Mars surface features. 4th ed. Mars Research, 1988.

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Schommers, W. Cosmic secrets: Basic features of reality. World Scientific, 2012.

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Davis, James E. Environmental satellites: Features and acquisition plans. Edited by Thompson Gregory F and United States. General Accounting Office. Nova Novinka, 2012.

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Eliseeva, Elena. Khudozhestvennoe prostranstvo v otechestvennykh igrovykh filʹmakh XX veka. "Starklaĭt", 2012.

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1965-, Carlson Laura Anne, and Zee Emile van der, eds. Functional features in language and space: Insights from perception, categorization, and development. Oxford University Press, 2005.

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J, Müller Hermann, and Deutsche Forschungsgemeinschaft, eds. Neural binding of space and time: Spatial and temporal mechanisms of feature-object binding. Psychology Press, 2001.

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Crompton, John L. The proximate principle: The impact of parks, open space and water features on residential property values and the property tax base. 2nd ed. National Recreation and Park Association, 2004.

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Doorn, Niels van. Digital spaces, material traces: Investigating the performance of gender, sexuality, and embodiment on internet platforms that feature user-generated content. [s.n.], 2009.

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Dynamic feature space modelling, filtering, and self-tuning control of stochastic systems: A systems approach with economic and social applications. Springer-Verlag, 1985.

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Capítulos de libros sobre el tema "Sparse features"

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Ranzato, Marc’Aurelio, Y.-Lan Boureau, Koray Kavukcuoglu, Karol Gregor, and Yann LeCun. "Learning Hierarchies of Sparse Features." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_1880.

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Zou, Yuan, and Teemu Roos. "Sparse Logistic Regression with Logical Features." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31753-3_26.

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Haker, Martin, Thomas Martinetz, and Erhardt Barth. "Multimodal Sparse Features for Object Detection." In Artificial Neural Networks – ICANN 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04277-5_93.

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Carneiro, Gustavo, and David Lowe. "Sparse Flexible Models of Local Features." In Computer Vision – ECCV 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744078_3.

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Ujaldon, M., E. L. Zapata, B. M. Chapman, and H. P. Zima. "Data-parallel Language Features for Sparse Codes." In Languages, Compilers and Run-Time Systems for Scalable Computers. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4615-2315-4_19.

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Rebecchi, Sébastien, Hélène Paugam-Moisy, and Michèle Sebag. "Learning Sparse Features with an Auto-Associator." In Growing Adaptive Machines. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55337-0_4.

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Barata, Catarina, Mário A. T. Figueiredo, M. Emre Celebi, and Jorge S. Marques. "Local Features Applied to Dermoscopy Images: Bag-of-Features versus Sparse Coding." In Pattern Recognition and Image Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58838-4_58.

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Gaur, Yashesh, Maulik C. Madhavi, and Hemant A. Patil. "Speaker Recognition Using Sparse Representation via Superimposed Features." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45062-4_19.

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Zhang, Ziming, Jiawei Huang, and Ze-Nian Li. "Learning Sparse Features On-Line for Image Classification." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21593-3_13.

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Du, Jianhao, Weihua Sheng, Qi Cheng, and Meiqin Liu. "Proactive 3D Robot Mapping in Environments with Sparse Features." In Advances in Visual Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14249-4_74.

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Actas de conferencias sobre el tema "Sparse features"

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Metwally, Ahmed, and Michael Shum. "Similarity Joins of Sparse Features." In SIGMOD/PODS '24: International Conference on Management of Data. ACM, 2024. http://dx.doi.org/10.1145/3626246.3653370.

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Chang, Jen-Hao Rick, Aswin C. Sankaranarayanan, and B. V. K. Vijaya Kumar. "Random Features for Sparse Signal Classification." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.583.

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Ge, Tiezheng, Qifa Ke, and Jian Sun. "Sparse-Coded Features for Image Retrieval." In British Machine Vision Conference 2013. British Machine Vision Association, 2013. http://dx.doi.org/10.5244/c.27.132.

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Iglesias, Gonzalo, Adrià de Gispert, and Bill Byrne. "Transducer Disambiguation with Sparse Topological Features." In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/d15-1273.

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Chakrabarti, Ayan, and Keigo Hirakawa. "Effective separation of sparse and non-sparse image features for denoising." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517745.

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Yin, Chong, Siqi Liu, Vincent Wai-Sun Wong, and Pong C. Yuen. "Learning Sparse Interpretable Features For NAS Scoring From Liver Biopsy Images." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/220.

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Liver biopsy images play a key role in the diagnosis of global non-alcoholic fatty liver disease (NAFLD). The NAFLD activity score (NAS) on liver biopsy images grades the amount of histological findings that reflect the progression of NAFLD. However, liver biopsy image analysis remains a challenging task due to its complex tissue structures and sparse distribution of histological findings. In this paper, we propose a sparse interpretable feature learning method (SparseX) to efficiently estimate NAS scores. First, we introduce an interpretable spatial sampling strategy based on histological fea
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Zhang, Xiaowang, Qiang Gao, and Zhiyong Feng. "InteractionNN: A Neural Network for Learning Hidden Features in Sparse Prediction." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/602.

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In this paper, we present a neural network (InteractionNN) for sparse predictive analysis where hidden features of sparse data can be learned by multilevel feature interaction. To characterize multilevel interaction of features, InteractionNN consists of three modules, namely, nonlinear interaction pooling, layer-lossing, and embedding. Nonlinear interaction pooling (NI pooling) is a hierarchical structure and, by shortcut connection, constructs low-level feature interactions from basic dense features to elementary features. Layer-lossing is a feed-forward neural network where high-level featu
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Kaarna, A. "Sparse Coded Spatial Features from Spectral Images." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.949.

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Ozcelikkale, Ayca. "Sparse Recovery with Non-Linear Fourier Features." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054050.

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Sainath, Tara N., David Nahamoo, Bhuvana Ramabhadran, Dimitri Kanevsky, Vaibhava Goel, and Parikshit M. Shah. "Exemplar-based Sparse Representation phone identification features." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947352.

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Informes sobre el tema "Sparse features"

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Borgwardt, Stefan, Walter Forkel, and Alisa Kovtunova. Finding New Diamonds: Temporal Minimal-World Query Answering over Sparse ABoxes. Technische Universität Dresden, 2019. http://dx.doi.org/10.25368/2023.223.

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Lightweight temporal ontology languages have become a very active field of research in recent years. Many real-world applications, like processing electronic health records (EHRs), inherently contain a temporal dimension, and require efficient reasoning algorithms. Moreover, since medical data is not recorded on a regular basis, reasoners must deal with sparse data with potentially large temporal gaps. In this paper, we introduce a temporal extension of the tractable language ELH⊥, which features a new class of convex diamond operators that can be used to bridge temporal gaps. We develop a com
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Blundell, S. Micro-terrain and canopy feature extraction by breakline and differencing analysis of gridded elevation models : identifying terrain model discontinuities with application to off-road mobility modeling. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40185.

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Elevation models derived from high-resolution airborne lidar scanners provide an added dimension for identification and extraction of micro-terrain features characterized by topographic discontinuities or breaklines. Gridded digital surface models created from first-return lidar pulses are often combined with lidar-derived bare-earth models to extract vegetation features by model differencing. However, vegetative canopy can also be extracted from the digital surface model alone through breakline analysis by taking advantage of the fine-scale changes in slope that are detectable in high-resolut
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Naikal, Nikhil, Allen Yang, and S. S. Sastry. Informative Feature Selection for Object Recognition via Sparse PCA. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada543168.

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Rainey, Katie, and Ana Ascencio. Sparse Representation and Dictionary Learning as Feature Extraction in Vessel Imagery. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada613963.

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Horvath, Ildiko. Investigating repeatable ionospheric features during large space storms and superstorms. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada609369.

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Veth, Mike, and Meir Pachter. Correspondence Search Mitigation Using Feature Space Anti-Aliasing. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada473005.

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Bonnie, David John, and Kyle E. Lamb. MarFS: A Scalable Near-POSIX Name Space over Cloud Objects – New Features. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1333128.

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Méndez-Vizcaíno, Juan C., and Nicolás Moreno-Arias. A Global Shock with Idiosyncratic Pains: State-Dependent Debt Limits for LATAM during the COVID-19 pandemic. Banco de la República, 2021. http://dx.doi.org/10.32468/be.1175.

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Fiscal sustainability in five of the largest Latin American economies is examined before and after the COVID-19 pandemic. For this purpose, the DSGE model in Bi(2012) and Hürtgen (2020) is used to estimate the Fiscal Limits and Fiscal Spaces for Peru, Chile, Mexico, Colombia, and Brazil. These estimates advance the empirical literature for Latin America on fiscal sustainability by offering new calculations stemming from a structural framework with alluring novel features: government default on the intensive margin; dynamic Laffer curves; utility-based stochastic discount factor; and a Markov-S
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Slotiuk, Tetiana. CONCEPT OF SOLUTIONS JOURNALISM MODEL: CONNOTION, FUNCTIONS, FEATURES OF FUNCTIONING. Ivan Franko National University of Lviv, 2021. http://dx.doi.org/10.30970/vjo.2021.50.11097.

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The article examines the main features, general characteristics and essence of the concept of solutions journalism. The basic principles of functioning of this model of journalism in the western press and in Ukraine are given. The list and features of activity of the organizations, institutes and editorial offices supporting development of journalism of solutions journalism. The purpose of the publication is to describe the Solutions Journalism model: its features, characteristics and features of functioning, to find out the difference in the understanding of the concept of «solutions journali
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Jackiewicz, Jason. Automatic Recognition of Solar Features for Developing Data Driven Prediction Models of Solar Activity and Space Weather. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada563097.

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