Journal articles: 'Kernel discrimination'

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Araus, J. L., T. Amaro, J. Casadesús, A. Asbati, and M. M. Nachit. "Relationships between ash content, carbon isotope discrimination and yield in durum wheat." Functional Plant Biology 25, no. 7 (1998): 835. http://dx.doi.org/10.1071/pp98071.

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The relationships between ash content, carbon isotope discrimination and yield were studied in durum wheat (Triticum durum Desf.) grown in a Mediterranean region (north-western Syria) under three different water regimes (hereafter referred to as environments). Ash content (on dry mass basis) was measured in the flag leaf about 3 weeks after anthesis (leaf ash) and in mature kernels (kernel ash), whereas Δ was analysed in the penultimate leaf at heading (leaf Δ) and in mature kernels (kernel Δ). Leaf Δ was weakly or not related with the other parameters. Leaf ash correlated positively with kernel Δ (P≤0.001), even in the driest environment, which gave a mean yield of 1.5 t ha-1. For the four parameters, correlations with yield remained significant (P≤0.001) after correcting for days to heading. All the parameters showed a higher broad-sense heritability than yield. The parameter that showed the best genetic correlation with grain yield was kernel ash (r2= 0.88), followed by kernel Δ (r2 = 0.69) and leaf ash (r2 = 0.64), whereas leaf Δ (r2 = 0.26) was the least correlated parameter. Except for kernel ash, these parameters always correlated positively with grain yield. The negative relationships of kernel ash (on dry mass basis) with yield and all the other parameters may be attributable to the finding that kernel ash was higher in those genotypes more affected by drought during grain filling. Thus, kernel ash was negatively related (P≤0.001) with total kernel mass per spike. Prediction of grain yield through multiple linear regression suggests that kernel ash can be used as complementary criterion to either kernel Δ or leaf ash.

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Bian, Lu Sha, Yong Fang Yao, Xiao Yuan Jing, Sheng Li, Jiang Yue Man, and Jie Sun. "Face Recognition Based on a Fast Kernel Discriminant Analysis Approach." Advanced Materials Research 433-440 (January 2012): 6205–11. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6205.

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The computational cost of kernel discrimination is usually higher than linear discrimination, making many kernel methods impractically slow. To overcome this disadvantage, several accelerated algorithms have been presented, which express kernel discriminant vectors using a part of mapped training samples that are selected by some criterions. However, they still need to calculate a large kernel matrix using all training samples, so they only save rather limited computing time. In this paper, we propose the fast and effective kernel discriminations based on the mapped mean samples (MMS). It calculates a small kernel matrix by constructing a few mean samples in input space, then expresses the kernel discriminant vectors using MMS. The proposed kernel approach is tested on the public AR and FERET face databases. Experimental results show that this approach is effective in both saving computing time and acquiring favorable recognition results.

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SAKAKIBARA, YASUBUMI, KRIS POPENDORF, NANA OGAWA, KIYOSHI ASAI, and KENGO SATO. "STEM KERNELS FOR RNA SEQUENCE ANALYSES." Journal of Bioinformatics and Computational Biology 05, no. 05 (October 2007): 1103–22. http://dx.doi.org/10.1142/s0219720007003028.

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Several computational methods based on stochastic context-free grammars have been developed for modeling and analyzing functional RNA sequences. These grammatical methods have succeeded in modeling typical secondary structures of RNA, and are used for structural alignment of RNA sequences. However, such stochastic models cannot sufficiently discriminate member sequences of an RNA family from nonmembers and hence detect noncoding RNA regions from genome sequences. A novel kernel function, stem kernel, for the discrimination and detection of functional RNA sequences using support vector machines (SVMs) is proposed. The stem kernel is a natural extension of the string kernel, specifically the all-subsequences kernel, and is tailored to measure the similarity of two RNA sequences from the viewpoint of secondary structures. The stem kernel examines all possible common base pairs and stem structures of arbitrary lengths, including pseudoknots between two RNA sequences, and calculates the inner product of common stem structure counts. An efficient algorithm is developed to calculate the stem kernels based on dynamic programming. The stem kernels are then applied to discriminate members of an RNA family from nonmembers using SVMs. The study indicates that the discrimination ability of the stem kernel is strong compared with conventional methods. Furthermore, the potential application of the stem kernel is demonstrated by the detection of remotely homologous RNA families in terms of secondary structures. This is because the string kernel is proven to work for the remote homology detection of protein sequences. These experimental results have convinced us to apply the stem kernel in order to find novel RNA families from genome sequences.

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Jirsa, Ondřej, and Ivana Polišenská. "Identification of Fusarium damaged wheat kernels using image analysis." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 59, no. 5 (2011): 125–30. http://dx.doi.org/10.11118/actaun201159050125.

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Visual evaluation of kernels damaged by Fusarium spp. pathogens is labour intensive and due to a subjective approach, it can lead to inconsistencies. Digital imaging technology combined with appropriate statistical methods can provide much faster and more accurate evaluation of the visually scabby kernels proportion. The aim of the present study was to develop a discrimination model to identify wheat kernels infected by Fusarium spp. using digital image analysis and statistical methods. Winter wheat kernels from field experiments were evaluated visually as healthy or damaged. Deoxynivalenol (DON) content was determined in individual kernels using an ELISA method. Images of individual kernels were produced using a digital camera on dark background. Colour and shape descriptors were obtained by image analysis from the area representing the kernel. Healthy and damaged kernels differed significantly in DON content and kernel weight. Various combinations of individual shape and colour descriptors were examined during the development of the model using linear discriminant analysis. In addition to basic descriptors of the RGB colour model (red, green, blue), very good classification was also obtained using hue from the HSL colour model (hue, saturation, luminance). The accuracy of classification using the developed discrimination model based on RGBH descriptors was 85 %. The shape descriptors themselves were not specific enough to distinguish individual kernels.

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Troshchynska, Yana, Roman Bleha, Lenka Kumbarová, Marcela Sluková, Andrej Sinica, and Jiří Štětina. "Characterisation of flaxseed cultivars based on NIR diffusion reflectance spectra of whole seeds and derived samples." Czech Journal of Food Sciences 37, No. 5 (October 31, 2019): 374–82. http://dx.doi.org/10.17221/270/2018-cjfs.

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Discrimination of yellow and brown flaxseed cultivars was made based on diffusion reflectance FT-NIR spectra of whole seeds. The spectra of flaxseed kernels, hulls, defatted flours, and oils were also measured for comparison. Hierarchy cluster analysis (HCA) and principal component analysis (PCA) were used for the discrimination. Multivariate analyses of FT-NIR spectra led to satisfactory discrimination of all flaxseed cultivars of this study mainly according to the nutritionally important fatty acid composition that was confirmed by comparison with the corresponding spectra of flaxseed kernel and oil. By contrast, spectral features of proteins, polysaccharides, and tannins predominated in the FT-NIR spectra of flaxseed hulls and defatted flours.

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El-Sebai, Osama A., Robert Sanderson, Max P. Bleiweiss, and Naomi Schmidt. "Detection of Sitotroga cerealella (Olivier) infestation of Wheat Kernels Using Hyperspectral Reflectance." Journal of Entomological Science 41, no. 2 (April 1, 2006): 155–64. http://dx.doi.org/10.18474/0749-8004-41.2.155.

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Hyperspectral reflectance data were used to detect internal infestations of Angoumois grain moth, Sitotroga ceralella (Olivier), in wheat kernels. Kernel reflectance was measured with a spectroradiometer over a wavelength range of 350–2500 nm. Kernel samples were selected randomly and scanned every 7 d after infestation to determine the ability of the hyperspectral reflectance data to discriminate between infested and uninfested kernels. Immature stages of S. ceralella inside wheat kernels can be detected through changes in moisture, starch, and chitin content of the kernel. By using the spectrally-derived moisture variable (Log[1/R972nm]-Log[1/R1032nm]) and starch variable (Log[1/R982nm]-Log[1/R1014nm]), it was possible to discriminate between infested and uninfested wheat kernels with 100% classification accuracy based on 90% confidence intervals. Significant differences in the spectral reflectance between the infested and uninfested kernels were due to changes in moisture and starch content in wheat kernels. Three of the four chitin variables showed slight discrimination between the infested and uninfested wheat kernels based on 90% confidence intervals with 63.9%, 68.8%, 66.7%, and 41.6% classification accuracy of the three variables (Log[1/R1130nm]-Log[1/R1670nm]), (Log[1/R1139nm ]-Log[1/R1320nm]), (Log[1/R1202nm]-Log[1/R1300nm]), and (Log[1/R2046nm]-Log[1/R2302nm]), respectively. Spectral reflectance changes as a function of wheat kernel position relative to the spectroradiometer sensor did not differ significantly (P > 0.10).

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SONG, HAN, FENG LI, PEIWEN GUANG, XINHAO YANG, HUANYU PAN, and FURONG HUANG. "Detection of Aflatoxin B1 in Peanut Oil Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis and Support Vector Machine Models." Journal of Food Protection 84, no. 8 (March 12, 2021): 1315–20. http://dx.doi.org/10.4315/jfp-20-447.

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ABSTRACT This study was conducted to establish a rapid and accurate method for identifying aflatoxin contamination in peanut oil. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with either partial least squares discriminant analysis (PLS-DA) or a support vector machine (SVM) algorithm were used to construct discriminative models for distinguishing between uncontaminated and aflatoxin-contaminated peanut oil. Peanut oil samples containing various concentrations of aflatoxin B1 were examined with an ATR-FTIR spectrometer. Preprocessed spectral data were input to PLS-DA and SVM algorithms to construct discriminative models for aflatoxin contamination in peanut oil. SVM penalty and kernel function parameters were optimized using grid search, a genetic algorithm, and particle swarm optimization. The PLS-DA model established using spectral data had an accuracy of 94.64% and better discrimination than did models established based on preprocessed data. The SVM model established after data normalization and grid search optimization with a penalty parameter of 16 and a kernel function parameter of 0.0359 had the best discrimination, with 98.2143% accuracy. The discriminative models for aflatoxin contamination in peanut oil established by combining ATR-FTIR spectral data and nonlinear SVM algorithm were superior to the linear PLS-DA models. HIGHLIGHTS

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Chen, Beining, Robert F. Harrison, Jérôme Hert, Chido Mpanhanga, Peter Willett, and David J. Wilton. "Ligand-based virtual screening using binary kernel discrimination." Molecular Simulation 31, no. 8 (July 2005): 597–604. http://dx.doi.org/10.1080/08927020500134177.

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Ćwiklińska-Jurkowska, Małgorzata M. "Visualization and Comparison of Single and Combined Parametric and Nonparametric Discriminant Methods for Leukemia Type Recognition Based on Gene Expression." Studies in Logic, Grammar and Rhetoric 43, no. 1 (December 1, 2015): 73–99. http://dx.doi.org/10.1515/slgr-2015-0043.

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Abstract A gene expression data set, containing 3051 genes and 38 tumor mRNA training samples, from a leukemia microarray study, was used for differentiation between ALL and AML groups of leukemia. In this paper, single and combined discriminant methods were applied on the basis of the selected few most discriminative variables according to Wilks’ lambda or the leave-one-out error of first nearest neighbor classifier. For the linear, quadratic, regularized, uncorrelated discrimination, kernel, nearest neighbor and naive Bayesian classifiers, two-dimensional graphs of the boundaries and discriminant functions for diagnostics are presented. Cross-validation and leave-one-out errors were used as measures of classifier performance to support diagnosis coming from this genomic data set. A small number of best discriminating genes, from two to ten, was sufficient to build discriminant methods of good performance. Especially useful were nearest neighbor methods. The results presented herein were comparable with outcomes obtained by other authors for larger numbers of applied genes. The linear, quadratic, uncorrelated Bayesian and regularized discrimination methods were subjected to bagging or boosting in order to assess the accuracy of the fusion. A conclusion drawn from the analysis was that resampling ensembles were not beneficial for two-dimensional discrimination.

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Chao, Guoqing, and Shiliang Sun. "Multi-kernel maximum entropy discrimination for multi-view learning." Intelligent Data Analysis 20, no. 3 (April 20, 2016): 481–93. http://dx.doi.org/10.3233/ida-160816.

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Willett, Peter, David Wilton, Basil Hartzoulakis, Raymond Tang, John Ford, and David Madge. "Prediction of Ion Channel Activity Using Binary Kernel Discrimination." Journal of Chemical Information and Modeling 47, no. 5 (July 10, 2007): 1961–66. http://dx.doi.org/10.1021/ci700087v.

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Tutz, Gerhard. "On cross-validation for discrete kernel estimates in discrimination." Communications in Statistics - Theory and Methods 18, no. 11 (January 1989): 4145–62. http://dx.doi.org/10.1080/03610928908830147.

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Koch, Inge, Kanta Naito, and Hiroaki Tanaka. "Kernel naive Bayes discrimination for high‐dimensional pattern recognition." Australian & New Zealand Journal of Statistics 61, no. 4 (December 2019): 401–28. http://dx.doi.org/10.1111/anzs.12279.

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Cao, Yice, Yan Wu, Ming Li, Wenkai Liang, and Peng Zhang. "PolSAR Image Classification Using a Superpixel-Based Composite Kernel and Elastic Net." Remote Sensing 13, no. 3 (January 22, 2021): 380. http://dx.doi.org/10.3390/rs13030380.

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The presence of speckles and the absence of discriminative features make it difficult for the pixel-level polarimetric synthetic aperture radar (PolSAR) image classification to achieve more accurate and coherent interpretation results, especially in the case of limited available training samples. To this end, this paper presents a composite kernel-based elastic net classifier (CK-ENC) for better PolSAR image classification. First, based on superpixel segmentation of different scales, three types of features are extracted to consider more discriminative information, thereby effectively suppressing the interference of speckles and achieving better target contour preservation. Then, a composite kernel (CK) is constructed to map these features and effectively implement feature fusion under the kernel framework. The CK exploits the correlation and diversity between different features to improve the representation and discrimination capabilities of features. Finally, an ENC integrated with CK (CK-ENC) is proposed to achieve better PolSAR image classification performance with limited training samples. Experimental results on airborne and spaceborne PolSAR datasets demonstrate that the proposed CK-ENC can achieve better visual coherence and yield higher classification accuracies than other state-of-art methods, especially in the case of limited training samples.

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Ghaedian, Ahmad R., and Randy L. Wehling. "Discrimination of Sound and Granary-Weevil-Larva-Infested Wheat Kernels by Near-Infrared Diffuse Reflectance Spectroscopy." Journal of AOAC INTERNATIONAL 80, no. 5 (September 1, 1997): 997–1005. http://dx.doi.org/10.1093/jaoac/80.5.997.

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Abstract Sound and infested wheat kernels containing lateinstar granary weevil larvae, as identified by X-ray analysis, were used to evaluate the ability of nearinfrared (NIR) spectroscopy to predict the presence of insect larvae in individual wheat kernels. Diffuse reflectance spectra at 1100-2500 nm were recorded from individual infested and sound kernels. Principal component analysis (PCA) of NIR spectra from sound kernels was used to construct calibration models by calculation of Mahalanobis distances. Calibration models were then applied to spectra obtainedfrom both sound and infested kernels in a separate validation set. A 5-factor PCA model using data from a first-derivative spectral transformation was the best model for correctly classifying kernels in an expanded validation sample set, including 100% of sound, 93% of infested, 95% of sound air dried, 86% of infested air dried kernels, and 90% of sound kernels from 6 wheat varieties. Calibrations using the spectral region from 1100 to 1900 nm were least sensitive to kernel moisture differences. Similar results were obtained when discriminant analysis was applied to log 1/R data from selected discrete wavelengths of NIR spectra.

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Galindo-Noreña, Steven, David Cárdenas-Peña, and Álvaro Orozco-Gutierrez. "Multiple Kernel Stein Spatial Patterns for the Multiclass Discrimination of Motor Imagery Tasks." Applied Sciences 10, no. 23 (December 2, 2020): 8628. http://dx.doi.org/10.3390/app10238628.

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Brain–computer interface (BCI) systems communicate the human brain and computers by converting electrical activity into commands to use external devices. Such kind of system has become an alternative for interaction with the environment for people suffering from motor disabilities through the motor imagery (MI) paradigm. Despite being the most widespread, electroencephalography (EEG)-based MI systems are highly sensitive to noise and artifacts. Further, spatially close brain activity sources and variability among subjects hampers the system performance. This work proposes a methodology for the classification of EEG signals, termed Multiple Kernel Stein Spatial Patterns (MKSSP) dealing with noise, raveled brain activity, and subject variability issues. Firstly, a bank of bandpass filters decomposes brain activity into spectrally independent multichannel signals. Then, Multi-Kernel Stein Spatial Patterns (MKSSP) maps each signal into low-dimensional covariance matrices preserving the nonlinear channel relationships. The Stein kernel provides a parameterized similarity metric for covariance matrices that belong to a Riemannian manifold. Lastly, the multiple kernel learning assembles the similarities from each spectral decomposition into a single kernel matrix that feeds the classifier. Experimental evaluations in the well-known four-class MI dataset 2a BCI competition IV proves that the methodology significantly improves state-of-the-art approaches. Further, the proposal is interpretable in terms of data distribution, spectral relevance, and spatial patterns. Such interpretability demonstrates that MKSSP encodes features from different spectral bands into a single representation improving the discrimination of mental tasks.

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Wilton, David J., Robert F. Harrison, Peter Willett, John Delaney, Kevin Lawson, and Graham Mullier. "Virtual Screening Using Binary Kernel Discrimination: Analysis of Pesticide Data." Journal of Chemical Information and Modeling 46, no. 2 (March 2006): 471–77. http://dx.doi.org/10.1021/ci050397w.

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Beiji, Zou, Nurudeen Mohammed, Zhu Chengzhang, Wang Lei, and Zhao Rongchang. "Overall Gabor Classifier (OGC) with Kernel Partial Least Square Discrimination." Journal of Computational and Theoretical Nanoscience 14, no. 8 (August 1, 2017): 3727–36. http://dx.doi.org/10.1166/jctn.2017.6665.

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Wang, Boxiang, and Hui Zou. "A Multicategory Kernel Distance Weighted Discrimination Method for Multiclass Classification." Technometrics 61, no. 3 (March 22, 2019): 396–408. http://dx.doi.org/10.1080/00401706.2018.1529629.

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Harrison, Robert F., and Kitsuchart Pasupa. "A simple iterative algorithm for parsimonious binary kernel Fisher discrimination." Pattern Analysis and Applications 13, no. 1 (June 30, 2009): 15–22. http://dx.doi.org/10.1007/s10044-009-0162-1.

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Ma, Ying, Ke Qin, and Shunzhi Zhu. "Discrimination Analysis for Predicting Defect-Prone Software Modules." Journal of Applied Mathematics 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/675368.

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Software defect prediction studies usually build models without analyzing the data used in the procedure. As a result, the same approach has different performances on different data sets. In this paper, we introduce discrimination analysis for providing a good method to give insight into the inherent property of the software data. Based on the analysis, we find that the data sets used in this field have nonlinearly separable and class-imbalanced problems. Unlike the prior works, we try to exploit the kernel method to nonlinearly map the data into a high-dimensional feature space. By combating these two problems, we propose an algorithm based on kernel discrimination analysis called KDC to build more effective prediction model. Experimental results on the data sets from different organizations indicate that KDC is more accurate in terms ofF-measure than the state-of-the-art methods. We are optimistic that our discrimination analysis method can guide more studies on data structure, which may derive useful knowledge from data science for building more accurate prediction models.

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Štruc, Vitomir, and Nikola Pavešić. "Gabor-Based Kernel Partial-Least-Squares Discrimination Features for Face Recognition." Informatica 20, no. 1 (January 1, 2009): 115–38. http://dx.doi.org/10.15388/informatica.2009.240.

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Ahn, Jeongyoun. "A stable hyperparameter selection for the Gaussian RBF kernel for discrimination." Statistical Analysis and Data Mining: The ASA Data Science Journal 3, no. 3 (May 4, 2010): 142–48. http://dx.doi.org/10.1002/sam.10073.

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Min, Beomjun, Jongin Kim, Hyeong-jun Park, and Boreom Lee. "Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram." BioMed Research International 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/2618265.

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The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two variants of an extreme learning machine with different kernels. Because each single trial consisted of thirty segments, our algorithm decided the label of the single trial by selecting the most frequent output among the outputs of the thirty segments. As a result, we observed that the extreme learning machine and its variants achieved better classification rates than the support vector machine with a radial basis function kernel and linear discrimination analysis. Thus, our results suggested that EEG responses to imagined speech could be successfully classified in a single trial using an extreme learning machine with a radial basis function and linear kernel. This study with classification of imagined speech might contribute to the development of silent speech BCI systems.

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Nomura, Masaki, Yoshio Sakurai, and Toshio Aoyagi. "Analysis of Multineuron Activity Using the Kernel Method." Journal of Robotics and Mechatronics 19, no. 4 (August 20, 2007): 364–68. http://dx.doi.org/10.20965/jrm.2007.p0364.

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We recorded multineuron spike time-series data from rat hippocampus region CA1 during a conditional discrimination task. We separated out individual single-neuron activity from multineuron activity data and prepared spike count data and calculated a kernel matrix using a Spikernel function, then applied k-means clustering and principal component analysis (PCA). Comparing spike count data to an appropriate time, we divided data into clusters and found the correspondence between the obtained cluster and rat activity. We discuss information expression in nervous-system activity expected from the kernel function.

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Fan, Zhong Jie, Yan Qiu Leng, Yong Long Xu, Zheng Jiang Meng, and Ji Wei Xu. "A Discrimination Method of Saturated Sand Liquefaction Possibility Based on Support Vector Machine." Applied Mechanics and Materials 509 (February 2014): 38–43. http://dx.doi.org/10.4028/www.scientific.net/amm.509.38.

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Based on the analysis of influence factors of saturated sand, this paper expounds the limitations of traditional evaluation of liquefaction, and introduces the criterion of support vector machine (SVM) based on the principle of structural risk minimization. According to the main influence factors of sand liquefaction, a SVM discriminant model of sand liquefaction with different kernel functions is established. Through studying small sample data, this model can establish nonlinear mapping relationship between influence factors and liquefaction type. On the basis of seismic data, a radial based kernel function is selected to predict sand liquefaction type. The research results show that the predicted magnitude is identical with the actual result, to prove that it is effective to apply this SVM model to evaluate the level of sand liquefaction.

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Royo, C., D. Villegas, L. F. García del Moral, S. Elhani, N. Aparicio, Y. Rharrabti, and J. L. Araus. "Comparative performance of carbon isotope discrimination and canopy temperature depression as predictors of genotype differences in durum wheat yield in Spain." Australian Journal of Agricultural Research 53, no. 5 (2002): 561. http://dx.doi.org/10.1071/ar01016.

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The relationships between carbon isotope discrimination (Δ) in mature kernels, canopy temperature depression (CTD) during anthesis and grain filling, 1000-kernel weight (TKW), total carbon content of mature kernels, and yield were studied in durum wheat (Triticum turgidum L. var. durum) grown in Spain (western Mediterranean basin). Twenty-five durum wheat genotypes were grown in 2 regions (NE and SE Spain) and under 2 water regimes (rainfed v. support irrigation) from 1997 to 1999 (i.e. a total of 12 trials). Principal component analysis placed yield and Δ on the same axis. Pearson’s correlation and stepwise analysis confirmed that Δ was the trait that best assessed genotype differences in yield within trials, and was followed, at a considerable distance, by TKW. Our results also demonstrated the extremely poor performance of CTD throughout the wide range of growing conditions in this study.

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SCHLEIF, F. M., THOMAS VILLMANN, BARBARA HAMMER, and PETRA SCHNEIDER. "EFFICIENT KERNELIZED PROTOTYPE BASED CLASSIFICATION." International Journal of Neural Systems 21, no. 06 (December 2011): 443–57. http://dx.doi.org/10.1142/s012906571100295x.

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Prototype based classifiers are effective algorithms in modeling classification problems and have been applied in multiple domains. While many supervised learning algorithms have been successfully extended to kernels to improve the discrimination power by means of the kernel concept, prototype based classifiers are typically still used with Euclidean distance measures. Kernelized variants of prototype based classifiers are currently too complex to be applied for larger data sets. Here we propose an extension of Kernelized Generalized Learning Vector Quantization (KGLVQ) employing a sparsity and approximation technique to reduce the learning complexity. We provide generalization error bounds and experimental results on real world data, showing that the extended approach is comparable to SVM on different public data.

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Harper, Gavin, John Bradshaw, John C. Gittins, Darren V. S. Green, and Andrew R. Leach. "Prediction of Biological Activity for High-Throughput Screening Using Binary Kernel Discrimination." Journal of Chemical Information and Computer Sciences 41, no. 5 (September 2001): 1295–300. http://dx.doi.org/10.1021/ci000397q.

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Pilario, Karl Ezra, Alexander Tielemans, and Elmer-Rico E. Mojica. "Geographical discrimination of propolis using dynamic time warping kernel principal components analysis." Expert Systems with Applications 187 (January 2022): 115938. http://dx.doi.org/10.1016/j.eswa.2021.115938.

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Fazzini, Paolo, Giuseppina De Felice Proia, Maria Adamo, Palma Blonda, Francesco Petracchini, Luigi Forte, and Cristina Tarantino. "Sentinel-2 Remote Sensed Image Classification with Patchwise Trained ConvNets for Grassland Habitat Discrimination." Remote Sensing 13, no. 12 (June 10, 2021): 2276. http://dx.doi.org/10.3390/rs13122276.

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The present study focuses on the use of Convolutional Neural Networks (CNN or ConvNet) to classify a multi-seasonal dataset of Sentinel-2 images to discriminate four grassland habitats in the “Murgia Alta” protected site. To this end, we compared two approaches differing only by the first layer machinery, which, in one case, is instantiated as a fully-connected layer and, in the other case, results in a ConvNet equipped with kernels covering the whole input (wide-kernel ConvNet). A patchwise approach, tessellating training reference data in square patches, was adopted. Besides assessing the effectiveness of ConvNets with patched multispectral data, we analyzed how the information needed for classification spreads to patterns over convex sets of pixels. Our results show that: (a) with an F1-score of around 97% (5 × 5 patch size), ConvNets provides an excellent tool for patch-based pattern recognition with multispectral input data without requiring special feature extraction; (b) the information spreads over the limit of a single pixel: the performance of the network increases until 5 × 5 patch sizes are used and then ConvNet performance starts decreasing.

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Bourouhou, Abdelhamid, Abdelilah Jilbab, Chafik Nacir, and Ahmed Hammouch. "Heart Sounds Classification for a Medical Diagnostic Assistance." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 11 (July 16, 2019): 88. http://dx.doi.org/10.3991/ijoe.v15i11.10804.

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<span lang="EN-US">In order to develop the assessment of phonocardiogram “PCG” signal for discrimination between two of people classes – individuals with heart disease and healthy one- we have adopted the database provided by "The PhysioNet/Computing in Cardilogy Challenge 2016", which contains records of heart sounds 'PCG '. This database is chosen in order to compare and validate our results with those already published. We subsequently extracted 20 features from each provided record. For classification, we used the Generalized Linear Model (GLM), and the Support Vector Machines (SVMs) with its different types of kernels (i.e.; Linear, polynomial and MLP). The best classification accuracy obtained was 88.25%, using the SVM classifier with an MLP kernel.</span>

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Yang, Yu-Qian, and Cheng-Yi Zhang. "Kernel Based Telegraph-Diffusion Equation for Image Noise Removal." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/283751.

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The second-order partial differential equations have good performances on noise smoothing and edge preservation. However, for low signal-to-noise ratio (SNR) images, the discrimination between edges and noise is a challenging problem. In this paper, the authors propose a kernel based telegraph-diffusion equation (KTDE) for noise removal. In this method, a kernelized gradient operator is introduced in the second-order telegraph-diffusion equation (TDE), which leads to more effective noise removal capability. Experiment results show that this method outperforms several anisotropic diffusion methods and the TDE method for noise removal and edge preservation.

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Cárdenas-Peña, David, Diego Collazos-Huertas, and German Castellanos-Dominguez. "Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis." Computational and Mathematical Methods in Medicine 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9523849.

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Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014CADDementiachallenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing.

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Kar, Arindam, Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nasipuri, and Mahantapas Kundu. "A Gabor-Block-Based Kernel Discriminative Common Vector Approach Using Cosine Kernels for Human Face Recognition." Computational Intelligence and Neuroscience 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/421032.

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In this paper a nonlinear Gabor Wavelet Transform (GWT) discriminant feature extraction approach for enhanced face recognition is proposed. Firstly, the low-energized blocks from Gabor wavelet transformed images are extracted. Secondly, the nonlinear discriminating features are analyzed and extracted from the selected low-energized blocks by the generalized Kernel Discriminative Common Vector (KDCV) method. The KDCV method is extended to include cosine kernel function in the discriminating method. The KDCV with the cosine kernels is then applied on the extracted low-energized discriminating feature vectors to obtain the real component of a complex quantity for face recognition. In order to derive positive kernel discriminative vectors, we apply only those kernel discriminative eigenvectors that are associated with nonzero eigenvalues. The feasibility of the low-energized Gabor-block-based generalized KDCV method with cosine kernel function models has been successfully tested for classification using theL1, L2distance measures; and the cosine similarity measure on both frontal and pose-angled face recognition. Experimental results on the FRAV2D and the FERET database demonstrate the effectiveness of this new approach.

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Ghansah, Benjamin, Ben-Bright Benuwa, and Augustine Monney. "A Discriminative Locality-Sensitive Dictionary Learning With Kernel Weighted KNN Classification for Video Semantic Concepts Analysis." International Journal of Intelligent Information Technologies 17, no. 1 (January 2021): 68–91. http://dx.doi.org/10.4018/ijiit.2021010105.

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Video semantic concept analysis has received a lot of research attention in the area of human computer interactions in recent times. Reconstruction error classification methods based on sparse coefficients do not consider discrimination, essential for classification performance between video samples. To further improve the accuracy of video semantic classification, a video semantic concept classification approach based on sparse coefficient vector (SCV) and a kernel-based weighted KNN (KWKNN) is proposed in this paper. In the proposed approach, a loss function that integrates reconstruction error and discrimination is put forward. The authors calculate the loss function value between the test sample and training samples from each class according to the loss function criterion, and then vote on statistical results. Finally, this paper modifies the vote results combined with the kernel weight coefficient of each class and determine the video semantic concept. The experimental results show that this method effectively improves the classification accuracy for video semantic analysis and shorten the time used in the semantic classification compared with some baseline approaches.

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Feng, Xuping, Yiying Zhao, Chu Zhang, Peng Cheng, and Yong He. "Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis." Sensors 17, no. 8 (August 17, 2017): 1894. http://dx.doi.org/10.3390/s17081894.

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Lee, Kun-Chou. "RADAR TARGET RECOGNITION BY FREQUENCY-DIVERSITY RCS TOGETHER WITH KERNEL SCATTER DIFFERENCE DISCRIMINATION." Progress In Electromagnetics Research M 87 (2019): 137–45. http://dx.doi.org/10.2528/pierm19101201.

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REN Xiao-dong, 任晓东, and 雷武虎 LEI Wu-hu. "Kernel Anomaly Detection Method in Hyperspectral Imagery Based on the Spectral Discrimination Method." ACTA PHOTONICA SINICA 45, no. 3 (2016): 330003. http://dx.doi.org/10.3788/gzxb20164503.0330003.

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Ma, Fei, Ju Wang, Changhong Liu, Xuzhong Lu, Wei Chen, Conggui Chen, Jianbo Yang, and Lei Zheng. "Discrimination of Kernel Quality Characteristics for Sunflower Seeds Based on Multispectral Imaging Approach." Food Analytical Methods 8, no. 7 (November 14, 2014): 1629–36. http://dx.doi.org/10.1007/s12161-014-0038-x.

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Baek, Insuck, Dewi Kusumaningrum, Lalit Kandpal, Santosh Lohumi, Changyeun Mo, Moon Kim, and Byoung-Kwan Cho. "Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis." Sensors 19, no. 2 (January 11, 2019): 271. http://dx.doi.org/10.3390/s19020271.

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Viability is an important quality factor influencing seed germination and crop yield. Current seed-viability testing methods rely on conventional manual inspections, which use destructive, labor-intensive and time-consuming measurements. The aim of this study is to distinguish between viable and nonviable soybean seeds, using a near-infrared (NIR) hyperspectral imaging (HSI) technique in a rapid and nondestructive manner. The data extracted from the NIR–HSI of viable and nonviable soybean seeds were analyzed using a partial least-squares discrimination analysis (PLS-DA) technique for classifying the viable and nonviable soybean seeds. Variable importance in projection (VIP) was used as a waveband selection method to develop a multispectral imaging model. Initially, the spectral profile of each pixel in the soybean seed images was subjected to PLS-DA analysis, which yielded a reasonable classification accuracy; however, the pixel-based classification method was not successful for high accuracy detection for nonviable seeds. Another viability detection method was then investigated: a kernel image threshold method with an optimum-detection-rate strategy. The kernel-based classification of seeds showed over 95% accuracy even when using only seven optimal wavebands selected through VIP. The results show that the proposed multispectral NIR imaging method is an effective and accurate nondestructive technique for the discrimination of soybean seed viability.

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Liu, Zhenqiu, Dechang Chen, and Halima Bensmail. "Gene Expression Data Classification With Kernel Principal Component Analysis." Journal of Biomedicine and Biotechnology 2005, no. 2 (2005): 155–59. http://dx.doi.org/10.1155/jbb.2005.155.

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One important feature of the gene expression data is that the number of genesMfar exceeds the number of samplesN. Standard statistical methods do not work well whenN<M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of principal component analysis. The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.

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Sun, Yubing, Jun Wang, and Shaoming Cheng. "Early Diagnosis of Botrytis Cinerea Infestation of Tomato Plant by Electronic Nose." Applied Engineering in Agriculture 34, no. 4 (2018): 667–74. http://dx.doi.org/10.13031/aea.12748.

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Abstract. Early diagnosis of disease is important for loss control. It is much easier to manage and prevent disease from spreading in this period. This study employed electronic nose (E-nose) for early diagnosis of infestation of tomato plant. Gas Chromatography-Mass Spectrometer (GC-MS) was applied for proving the potential of E-nose detection and taken as the evidence for determining the range of parameters of Kernel Principal Component Analysis (KPCA). Then, the way to seek the best parameter (the type of kernel, kernel parameter, and the number of principal component) of KPCA for the prediction of time of tomato plant under disease attack was introduced. The results showed that when the type of kernel was Polynomial, the kernel parameter was 2, and the number of principal component was 17, the highest correct discrimination rate of Linear Discriminant Analysis (LDA) was obtained, which was as high as 100%. Furthermore, multiple linear regressions (MLR) was employed and the results showed that MLR combined with KPCA obtained excellent performance. This study demonstrated that it was feasible for early diagnosis of infestation of tomato plant by E-nose and the model for predicting the infestation time was presented. Keywords: Disease infestation, Early diagnosis, Sensors, Tomato plant.

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Kim, Sangkyeum, Kyunghyun Lee, and Kwanho You. "Seismic Discrimination between Earthquakes and Explosions Using Support Vector Machine." Sensors 20, no. 7 (March 28, 2020): 1879. http://dx.doi.org/10.3390/s20071879.

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The discrimination between earthquakes and explosions is a serious issue in seismic signal analysis. This paper proposes a seismic discrimination method using support vector machine (SVM), wherein the amplitudes of the P-wave and the S-wave of the seismic signals are selected as feature vectors. Furthermore, to improve the seismic discrimination performance using a heterodyne laser interferometer for seismic wave detection, the Hough transform is applied as a compensation method for the periodic nonlinearity error caused by the frequency-mixing in the laser interferometric seismometer. In the testing procedure, different kernel functions of SVM are used to discriminate between earthquakes and explosions. The outstanding performance of a laser interferometer and Hough transform method for precision seismic measurement and nonlinearity error compensation is confirmed through some experiments using a linear vibration stage. In addition, the effectiveness of the proposed discrimination method using a heterodyne laser interferometer is verified through a receiver operating characteristic curve and other performance indices obtained from practical experiments.

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Goriewa-Duba, Klaudia, Adrian Duba, Urszula Wachowska, and Marian Wiwart. "An Evaluation of the Variation in the Morphometric Parameters of Grain of Six Triticum Species with the Use of Digital Image Analysis." Agronomy 8, no. 12 (December 7, 2018): 296. http://dx.doi.org/10.3390/agronomy8120296.

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Kernel images of six wheat species were subjected to shape and color analyses to determine variations in the morphometric parameters of grain. The values of kernel shape descriptors (area, perimeter, Feret diameter, minimal Feret diameter, circularity, aspect ratio, roundness, solidity) and color descriptors (H, S, I and L*a*b*) were investigated. The influence of grain colonization by endophytic fungi on the color of the seed coat was also evaluated. Polish wheat grain was characterized by the highest intraspecific variation in shape and color. Bread wheat was most homogeneous in terms of the studied shape and color descriptors. An analysis of variations in wheat lines revealed greater differences in phenotypic traits of relict wheats, which have a larger gene pool. The grain of ancient wheat species was characterized by low roundness values and relatively low solidity. Shape and color descriptors were strongly discriminating components in the studied wheat species. Their discriminatory power was determined mainly by genotype. A method that supports rapid discrimination of cereal species and admixtures of other cereals in grain batches is required to guarantee the quality and safety of grain. The results of this study indicate that digital image analysis can be effectively used for this purpose.

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Torres-Valencia, Cristian, Álvaro Orozco, David Cárdenas-Peña, Andrés Álvarez-Meza, and Mauricio Álvarez. "A Discriminative Multi-Output Gaussian Processes Scheme for Brain Electrical Activity Analysis." Applied Sciences 10, no. 19 (September 27, 2020): 6765. http://dx.doi.org/10.3390/app10196765.

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The study of brain electrical activity (BEA) from different cognitive conditions has attracted a lot of interest in the last decade due to the high number of possible applications that could be generated from it. In this work, a discriminative framework for BEA via electroencephalography (EEG) is proposed based on multi-output Gaussian Processes (MOGPs) with a specialized spectral kernel. First, a signal segmentation stage is executed, and the channels from the EEG are used as the model outputs. Then, a novel covariance function within the MOGP known as the multispectral mixture kernel (MOSM) allows us to find and quantify the relationships between different channels. Several MOGPs are trained from different conditions grouped in bi-class problems, and the discrimination is performed based on the likelihood score of the test signals against all the models. Finally, the mean likelihood is computed to predict the correspondence of new inputs with each class’s existing models. Results show that this framework allows us to model the EEG signals adequately using generative models and allows analyzing the relationships between channels of the EEG for a particular condition. At the same time, the set of trained MOGPs is well suited to discriminate new input data.

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Crnojević, Vladimir, Marko Panić, Branko Brkljač, Dubravko Ćulibrk, Jelena Ačanski, and Ante Vujić. "Image Processing Method for Automatic Discrimination of Hoverfly Species." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/986271.

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An approach to automatic hoverfly species discrimination based on detection and extraction of vein junctions in wing venation patterns of insects is presented in the paper. The dataset used in our experiments consists of high resolution microscopic wing images of several hoverfly species collected over a relatively long period of time at different geographic locations. Junctions are detected using the combination of the well known HOG (histograms of oriented gradients) and the robust version of recently proposed CLBP (complete local binary pattern). These features are used to train an SVM classifier to detect junctions in wing images. Once the junctions are identified they are used to extract statistics characterizing the constellations of these points. Such simple features can be used to automatically discriminate four selected hoverfly species with polynomial kernel SVM and achieve high classification accuracy.

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Harper, Gavin, John Bradshaw, John C. Gittins, Darren V. S. Green, and Andrew R. Leach. "ChemInform Abstract: Prediction of Biological Activity for High-Throughput Screening Using Binary Kernel Discrimination." ChemInform 32, no. 48 (May 23, 2010): no. http://dx.doi.org/10.1002/chin.200148231.

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Labbé, Nicole, Seung-Hwan Lee, Hyun-Woo Cho, Myong K. Jeong, and Nicolas André. "Enhanced discrimination and calibration of biomass NIR spectral data using non-linear kernel methods." Bioresource Technology 99, no. 17 (November 2008): 8445–52. http://dx.doi.org/10.1016/j.biortech.2008.02.052.

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Tang, Yidong, Shucai Huang, and Aijun Xue. "Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/3460281.

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The sparse representation based classifier (SRC) and its kernel version (KSRC) have been employed for hyperspectral image (HSI) classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH) model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP) algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.

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Journal articles on the topic 'Kernel discrimination'

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