14.1.4.8 Discriminant Analysis

Chapter Contents (Back)
Discriminant Analysis. Linear Discriminant Analysis. LDA. Discriminant analysis determines which variables discriminate between two or more groups.
See also Invariants -- Linear Discriminant Analysis, Fisher Linear Discriminant.

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IEEE DOI 0606
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Earlier:
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IEEE DOI 0406
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Hamsici, O.C.[Onur C.], Martinez, A.M.[Aleix M.],
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Linear discriminant analysis; Feature extraction; Fisher criterion; Foley-Sammon transform
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IEEE DOI 0711
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Halbe, Z.[Zohar], Aladjem, M.[Mayer],
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IEEE DOI 1812
Tensile stress, Feature extraction, Hyperspectral imaging, Robustness, Dimensionality reduction, Data models, Classification, tensor BibRef

Deng, Y., Li, H., Song, X., Sun, Y., Zhang, X., Du, Q.,
Patch Tensor-Based Multigraph Embedding Framework for Dimensionality Reduction of Hyperspectral Images,
GeoRS(58), No. 3, March 2020, pp. 1630-1643.
IEEE DOI 2003
Feature extraction, Germanium, Hyperspectral imaging, Manifolds, Dimensionality reduction, Classification, tensor analysis BibRef

Zhang, M.M.[Meng-Meng], Li, W.[Wei], Du, Q.[Qian],
Diverse Region-Based CNN for Hyperspectral Image Classification,
IP(27), No. 6, June 2018, pp. 2623-2634.
IEEE DOI 1804
feature extraction, hyperspectral imaging, image classification, learning (artificial intelligence), neural nets, pattern recognition BibRef

Li, Y.[Yuan], Xu, Q.Z.[Qi-Zhi], Li, W.[Wei], Nie, J.Y.[Jin-Yan],
Automatic Clustering-Based Two-Branch CNN for Hyperspectral Image Classification,
GeoRS(59), No. 9, September 2021, pp. 7803-7816.
IEEE DOI 2109
Strips, Spectral shape, Convolution, Roads, Neural networks, Interference, Feature extraction, Automatic clustering, hyperspectral image (HSI) BibRef

Pan, L.[Lei], Li, H.C.[Heng-Chao], Deng, Y.J.[Yang-Jun], Zhang, F.[Fan], Chen, X.D.[Xiang-Dong], Du, Q.[Qian],
Hyperspectral Dimensionality Reduction by Tensor Sparse and Low-Rank Graph-Based Discriminant Analysis,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
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Pan, L.[Lei], Li, H.C.[Heng-Chao], Li, W.[Wei], Chen, X.D.[Xiang-Dong], Wu, G.N.[Guang-Ning], Du, Q.[Qian],
Discriminant Analysis of Hyperspectral Imagery Using Fast Kernel Sparse and Low-Rank Graph,
GeoRS(55), No. 11, November 2017, pp. 6085-6098.
IEEE DOI 1711
Hyperspectral imaging, Kernel, Laplace equations, Manifolds, Principal component analysis, Sparse matrices, Dimensionality reduction (DR), graph embedding (GE), hyperspectral image, kernel methods, sparse, and, low-rank, graph BibRef

Li, W.[Wei], Du, Q.[Qian],
Laplacian Regularized Collaborative Graph for Discriminant Analysis of Hyperspectral Imagery,
GeoRS(54), No. 12, December 2016, pp. 7066-7076.
IEEE DOI 1612
geophysical techniques BibRef

Li, W.[Wei], Liu, J., Du, Q.[Qian],
Sparse and Low-Rank Graph for Discriminant Analysis of Hyperspectral Imagery,
GeoRS(54), No. 7, July 2016, pp. 4094-4105.
IEEE DOI 1606
Dictionaries BibRef

Wang, H.X.[Hai-Xian], Lu, X., Hu, Z.[Zilan], Zheng, W.M.[Wen-Ming],
Fisher Discriminant Analysis With L1-Norm,
Cyber(44), No. 6, June 2014, pp. 828-842.
IEEE DOI 1406
Dispersion BibRef

Wang, H.X.[Hai-Xian], Zheng, W.M.[Wen-Ming], Hu, Z.[Zilan], Chen, S.B.[Si-Bao],
Local and Weighted Maximum Margin Discriminant Analysis,
CVPR07(1-8).
IEEE DOI 0706
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Li, L.Y.[Li-Yuan], Goh, W.[Weixun], Lim, J.H.[Joo Hwee], Pan, S.J.L.[Sinno Jia-Lin],
Extended Spectral Regression for efficient scene recognition,
PR(47), No. 9, 2014, pp. 2940-2951.
Elsevier DOI 1406
Spectral Regression BibRef

Liu, H.W.[Hua-Wen], Ma, Z.J.[Zong-Jie], Zhang, S.C.[Shi-Chao], Wu, X.D.[Xin-Dong],
Penalized partial least square discriminant analysis with for multi-label data,
PR(48), No. 5, 2015, pp. 1724-1733.
Elsevier DOI 1502
Partial least squares BibRef

Ding, C.T.[Chun-Tao], Zhang, L.[Li],
Double adjacency graphs-based discriminant neighborhood embedding,
PR(48), No. 5, 2015, pp. 1734-1742.
Elsevier DOI 1502
Supervised learning BibRef

Abou-Moustafa, K.T.[Karim T.], de la Torre, F.[Fernando], Ferrie, F.P.[Frank P.],
Pareto models for discriminative multiclass linear dimensionality reduction,
PR(48), No. 5, 2015, pp. 1863-1877.
Elsevier DOI 1502
Fisher discriminant analysis BibRef

Bose, S.[Smarajit], Pal, A.[Amita], SahaRay, R.[Rita], Nayak, J.[Jitadeepa],
Generalized quadratic discriminant analysis,
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Elsevier DOI 1505
Linear discriminant analysis BibRef

Li, H., Shen, C., van den Hengel, A.J.[Anton J.], Shi, Q.,
Worst Case Linear Discriminant Analysis as Scalable Semidefinite Feasibility Problems,
IP(24), No. 8, August 2015, pp. 2382-2392.
IEEE DOI 1505
Computational complexity BibRef

Han, X.X.[Xi-Xuan], Clemmensen, L.[Line],
Regularized generalized eigen-decomposition with applications to sparse supervised feature extraction and sparse discriminant analysis,
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Sparse discriminant analysis BibRef

Kan, M., Shan, S.G.[Shi-Guang], Zhang, H.H.[Hai-Hong], Lao, S.H.[Shi-Hong], Chen, X.L.[Xi-Lin],
Multi-View Discriminant Analysis,
PAMI(38), No. 1, January 2016, pp. 188-194.
IEEE DOI 1601
Bellows BibRef

Cui, Z.[Zhen], Shan, S.G.[Shi-Guang], Zhang, H.H.[Hai-Hong], Lao, S.H.[Shi-Hong], Chen, X.L.[Xi-Lin],
Structured Sparse Linear Discriminant Analysis,
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Wang, Y.[Ying], Ni, H.Y.[Hong-Yin], Liu, P.X.[Pei-Xun], Li, W.H.[Wen-Hui],
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Cherian, A.[Anoop], Morellas, V.[Vassilios], Papanikolopoulos, N.[Nikolaos],
Bayesian Nonparametric Clustering for Positive Definite Matrices,
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IEEE DOI 1604
Clustering algorithms BibRef

Cherian, A.[Anoop], Stanitsas, P.[Panagiotis], Harandi, M., Morellas, V.[Vassilios], Papanikolopoulos, N.[Nikolaos],
Learning Discriminative alpha-beta-Divergences for Positive Definite Matrices,
ICCV17(4280-4289)
IEEE DOI 1802
learning (artificial intelligence), matrix algebra, statistics, Dictionary Learning, Symmetric matrices BibRef

Cherian, A.[Anoop], Stanitsas, P.[Panagiotis], Wang, J.[Jue], Harandi, M.[Mehrtash], Morellas, V.[Vassilios], Papanikolopoulos, N.[Nikolaos],
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PAMI(44), No. 9, September 2022, pp. 5088-5102.
IEEE DOI 2208
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Earlier: A2, A1, A5, A6, Only:
Clustering Positive Definite Matrices by Learning Information Divergences,
Manifold17(1304-1312)
IEEE DOI 1802
Measurement, Symmetric matrices, Sparse matrices, Covariance matrices, Standards, Kernel, Geometry, texture recognition. Clustering algorithms, Geometry, Manifolds, Matrices, Measurement, Optimization BibRef

Sun, S., Xie, X., Yang, M.,
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Cyber(46), No. 12, December 2016, pp. 3272-3284.
IEEE DOI 1612
Correlation BibRef

Wu, G.[Gang], Feng, T.T.[Ting-Ting], Zhang, L.J.[Li-Jia], Yang, M.[Meng],
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Dimensionality reduction BibRef

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JVCIR(47), No. 1, 2017, pp. 10-22.
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Multilinear discriminant analysis BibRef

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Kernel subspace learning, Principal component analysis, Kernel discriminant analysis, Approximate kernel subspace learning BibRef

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Weighted Linear Discriminant Analysis Based on Class Saliency Information,
ICIP18(2306-2310)
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Estimation, Probabilistic logic, Visualization, Optimization, Linear discriminant analysis, Task analysis, Robustness, Linear Discriminant Analysis(LDA) BibRef

Wan, H.[Huan], Wang, H.[Hui], Guo, G.[Gongde], Wei, X.[Xin],
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PAMI(40), No. 2, February 2018, pp. 409-422.
IEEE DOI 1801
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Earlier: A1, A3, A2, A4:
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Liao, S., Gao, Q., Yang, Z., Chen, F., Nie, F., Han, J.,
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IEEE DOI 1809
Measurement, Robustness, Feature extraction, Principal component analysis, Transforms, Kernel, 1-norm BibRef

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Wang, L., Li, Q., Zhou, Y.,
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IP(28), No. 11, November 2019, pp. 5716-5728.
IEEE DOI 1909
Image segmentation, Semantics, Visualization, Proposals, Linear programming, Training, Support vector machines, region selection BibRef

Hu, P., Peng, D., Sang, Y., Xiang, Y.,
Multi-View Linear Discriminant Analysis Network,
IP(28), No. 11, November 2019, pp. 5352-5365.
IEEE DOI 1909
Correlation, Linear programming, Feature extraction, Kernel, Neural networks, Image reconstruction, multi-view representation learning BibRef

Zollanvari, A., Abdirash, M., Dadlani, A., Abibullaev, B.,
Asymptotically Bias-Corrected Regularized Linear Discriminant Analysis for Cost-Sensitive Binary Classification,
SPLetters(26), No. 9, September 2019, pp. 1300-1304.
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Gaussian distribution, matrix algebra, pattern classification, multivariate Gaussian distributions, regularization parameter, cost-sensitive classification BibRef

Song, X.[Xin], Jiang, X.W.[Xin-Wei], Gao, J.B.[Jun-Bin], Cai, Z.H.[Zhi-Hua],
Gaussian Process Graph-Based Discriminant Analysis for Hyperspectral Images Classification,
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Tzelepi, M.[Maria], Tefas, A.[Anastasios],
Improving the performance of lightweight CNNs for binary classification using quadratic mutual information regularization,
PR(106), 2020, pp. 107407.
Elsevier DOI 2006
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Earlier:
Discriminant Analysis Regularization in Lightweight Deep CNN Models,
ICIP19(3841-3845)
IEEE DOI 1910
Hinge loss, Cross entropy loss, Binary classification problems, Quadratic mutual information, Regularizer, Lightweight models, Deep learning. Discriminant Analysis Regularization, Convolutional Neural Networks, Drones. BibRef

Huang, H.H.[Hsin-Hsiung], Zhang, T.[Teng],
Robust discriminant analysis using multi-directional projection pursuit,
PRL(138), 2020, pp. 651-656.
Elsevier DOI 2010
Classification, Dimension reduction, Optimal scores, Projection pursuit, Robustness BibRef

Alarcón, Y.C.C.[Yonatan Carlos Carranza], Destercke, S.[Sébastien],
Imprecise Gaussian discriminant classification,
PR(112), 2021, pp. 107739.
Elsevier DOI 2102
Discriminant analysis, Robust Bayesian, Classification, Near-ignorance BibRef

Zheng, Z.C.[Zhi-Chao], Sun, H.J.[Huai-Jiang], Zhou, Y.[Ying],
Multiple discriminant analysis for collaborative representation-based classification,
PR(112), 2021, pp. 107819.
Elsevier DOI 2102
Collaborative representation, Orthogonal discriminative projection, Face recognition, Binary classification BibRef

Ghosh, A.[Abhik], SahaRay, R.[Rita], Chakrabarty, S.[Sayan], Bhadra, S.[Sayan],
Robust generalised quadratic discriminant analysis,
PR(117), 2021, pp. 107981.
Elsevier DOI 2106
Linear discriminant analysis, Quadratic discriminant analysis, Generalized quadratic discriminant analysis, Robust estimators BibRef

Kouw, W.M.[Wouter M.], Loog, M.[Marco],
Robust domain-adaptive discriminant analysis,
PRL(148), 2021, pp. 107-113.
Elsevier DOI 2107
Domain adaptation, Robust estimator, Discriminant analysis, Transduction BibRef

Zollanvari, A.[Amin], Abibullaev, B.[Berdakh],
Bias correction for linear discriminant analysis,
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Discriminant analysis, Bias-correction BibRef

Sofuoglu, S.E.[Seyyid Emre], Aviyente, S.[Selin],
Multi-Branch Tensor Network Structure for Tensor-Train Discriminant Analysis,
IP(30), 2021, pp. 8926-8938.
IEEE DOI 2111
Tensors, Feature extraction, Merging, Supervised learning, Computational complexity, Training, Matrix decomposition, supervised tensor-train analysis BibRef

Zhu, F.[Fa], Gao, J.B.[Jun-Bin], Yang, J.[Jian], Ye, N.[Ning],
Neighborhood linear discriminant analysis,
PR(123), 2022, pp. 108422.
Elsevier DOI 2112
Linear discriminant analysis, Reverse nearest neighbors, Neighborhood linear discriminant analysis, Multimodal class BibRef

Dufrenois, F.,
Incremental and compressible kernel null discriminant analysis,
PR(127), 2022, pp. 108642.
Elsevier DOI 2205
Incremental kernel discriminant analysis, Null space, Compression mechanism, Multi-class learning, Novelty detection BibRef

Chang, W.[Wei], Nie, F.P.[Fei-Ping], Wang, Z.[Zheng], Wang, R.[Rong], Li, X.L.[Xue-Long],
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PR(129), 2022, pp. 108778.
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Supervised dimensionality reduction, Linear discriminant analysis, Re-weighted method BibRef

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Discriminant analysis, Sufficient dimension reduction, Reproducing kernel Hilbert spaces, Support vector machine BibRef

Li, S.Y.[Shu-Yi], Zhang, H.M.[Heng-Min], Ma, R.J.[Rui-Jun], Zhou, J.H.[Jian-Hang], Wen, J.[Jie], Zhang, B.[Bob],
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PR(136), 2023, pp. 109196.
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Linear discriminant analysis, Kernel constraint, Intra-class and inter-class distance, Separability, Image classification BibRef

Kim, J.[Jiae], Lee, Y.[Yoonkyung], Liang, Z.[Zhiyu],
The Geometry of Nonlinear Embeddings in Kernel Discriminant Analysis,
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IEEE DOI 2303
Kernel, Sociology, Covariance matrices, Linear discriminant analysis, Geometry, spectral analysis BibRef

Liang, Z.Z.[Zhi-Zheng], Zhang, L.[Lei],
L1-norm discriminant analysis via Bhattacharyya error bounds under Laplace distributions,
PR(141), 2023, pp. 109609.
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Elsevier DOI 2311
Laplace distributions, Bhattacharyya error bound, Discriminant criteria, Kernel functions, Data classification BibRef

Min, K.Q.[Ke-Qian], Mai, Q.[Qing], Li, J.[Junge],
Optimality in high-dimensional tensor discriminant analysis,
PR(143), 2023, pp. 109803.
Elsevier DOI 2310
Discriminant analysis, Minimax optimality, Tensor BibRef


Franchi, G.[Gianni], Yu, X.L.[Xuan-Long], Bursuc, A.[Andrei], Aldea, E.[Emanuel], Dubuisson, S.[Severine], Filliat, D.[David],
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ECCV22(XII:243-260).
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Nagananda, N.[Navya], Savakis, A.[Andreas],
GILDA++: Grassmann Incremental Linear Discriminant Analysis,
Diff-CVML21(4448-4456)
IEEE DOI 2109
Manifolds, Training, Neural networks, Training data, Stochastic processes, Optimization methods, Eigenvalues and eigenfunctions BibRef

Chen, X.,
Improved Robust Discriminant Analysis for Feature Extraction,
ICPR18(1444-1449)
IEEE DOI 1812
Principal component analysis, Feature extraction, Optimization, Dispersion, Noise measurement, Complexity theory, I1-norm BibRef

Zhong, G., Zheng, Y., Zhang, X., Wei, H., Ling, X.,
Convolutional Discriminant Analysis,
ICPR18(1456-1461)
IEEE DOI 1812
Training, Task analysis, Convolutional neural networks, Face recognition, Optimization BibRef

Guo, M., Nie, F., Li, X.,
Self-Weighted Adaptive Locality Discriminant Analysis,
ICIP18(3378-3382)
IEEE DOI 1809
Data structures, Dimensionality reduction, Linear programming, Optical imaging, Linear discriminant analysis, Optimization, re-weighted method BibRef

Saglam, A., Baykan, N.A.,
A Satellite Image Classification Approach By Using One Dimensional Discriminant Analysis,
Gi4DM18(429-435).
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Ihou, K.E.[Koffi Eddy], Bouguila, N.[Nizar],
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belief networks, image classification, inference mechanisms, learning (artificial intelligence), CVB-LGDA, topic modeling BibRef

Liu, Y.Z.[Yan-Zhen], Bai, X.[Xiao], Yan, C.[Cheng], Zhou, J.[Jun],
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Discriminant Analysis; LDA; LPP; PCA; Sparse Representation; UDP BibRef

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Wang, S.Y.[Shu-Yang], Fu, Y.[Yun],
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Biometrics15(17-24)
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Chen, X.B.[Xiao-Bo], Yang, J.[Jian], Jin, Z.[Zhong],
An Improved Linear Discriminant Analysis with L1-Norm for Robust Feature Extraction,
ICPR14(1585-1590)
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Databases BibRef

Zhang, X.Y.[Xu-Yao], Liu, C.L.[Cheng-Lin],
Locally Smoothed Modified Quadratic Discriminant Function,
ICDAR13(8-12)
IEEE DOI 1312
covariance matrices BibRef

Sakano, H.[Hitoshi], Ohashi, T.[Tsukasa], Kimura, A.[Akisato], Sawada, H.[Hiroshi], Ishiguro, K.[Katsuhiko],
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Zafeiriou, S.P.[Stefanos P.],
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Su, T.H.[Tong-Hua], Liu, C.L.[Cheng-Lin], Zhang, X.Y.[Xu-Yao],
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Gao, H.Y.[Hao-Yuan], Zhuang, L.S.[Lian-Sheng], Yu, N.H.[Neng-Hai],
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Makihara, Y.S.[Yasu-Shi], Yagi, Y.S.[Yasu-Shi],
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Abou-Moustafa, K.T.[Karim T.], de la Torre, F.[Fernando], Ferrie, F.P.[Frank P.],
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Zhai, D.M.[De-Ming], Li, B.[Bo], Chang, H.[Hong], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin], Gao, W.[Wen],
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Zhang, L.[Li], Zhou, W.D.[Wei-Da], Zhang, H.[Hua], Jiao, L.C.[Li-Cheng],
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Nie, F.P.[Fei-Ping], Xiang, S.M.[Shi-Ming], Song, Y.Q.[Yang-Qiu], Zhang, C.S.[Chang-Shui],
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Ioffe, S.[Sergey],
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Xiong, T.[Tao], Ye, J.P.[Jie-Ping], Cherkassky, V.S.[Vladimir S.],
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Zhang, Q.A.[Qi-Ang], Liu, X.W.[Xiu-Wen],
Kernel Optimal Component Analysis,
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Chen, H.T.[Hwann-Tzong], Chang, H.W.[Huang-Wei], Liu, T.L.[Tyng-Luh],
Local Discriminant Embedding and Its Variants,
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Yan, S.C.[Shui-Cheng], Zhang, H.J.[Hong-Jiang], Hu, Y.X.[Yu-Xiao], Zhang, B.Y.[Ben-Yu], Cheng, Q.S.[Qian-Sheng],
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Hazaveh, K., Raahemifar, K.,
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Vasconcelos, N.M.[Nuno M.], Ho, P.[Purdy], Moreno, P.[Pedro],
The Kullback-Leibler Kernel as a Framework for Discriminant and Localized Representations for Visual Recognition,
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Vasconcelos, N.M.,
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Schulerud, H.,
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Gao, J., Ding, X.Q.[Xiao-Qing],
On Improvement of Feature Extraction Algorithms for Discriminative Pattern Classification,
ICPR00(Vol II: 101-104).
IEEE DOI 0009
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Huang, X.J.[Xiao-Jei], Wu, Y.S.[Yuo-Shou], Ding, X.Q.[Xiao-Qing],
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ICPR88(II: 1242-1244).
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Hiraoka, K., Hidai, K., Hamahira, M., Mizoguchi, H., Mishima, T., Yoshizawa, S.,
Successive Learning of Linear Discriminant Analysis: Sanger-type Algorithm,
ICPR00(Vol II: 664-667).
IEEE DOI 0009
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Training Set Size, Sample Size, Analysis, Selection .


Last update:Mar 16, 2024 at 20:36:19