1 D-LDA versus 2 D-LDA : When Is Vector-based Linear Discriminant Analysis Better than Matrix-based ?

1 School of Mathematics and Computation Science Sun Yat-sen University Guangzhou, P. R. China, wszheng@ieee.org 2 Department of Electronics & Communication Engineering, School of Information Science & Technology Sun Yat-sen University Guangzhou, P. R. China, stsljh@mail.sysu.edu.cn 3 Guangdong Province Key Laboratory of Information Security, P. R. China 4 Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China, szli@nlpr.ia.ac.cn

[1]  Hau-San Wong,et al.  Face recognition based on 2D Fisherface approach , 2006, Pattern Recognit..

[2]  Liwei Wang,et al.  On image matrix based feature extraction algorithms , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  E. K. Teoh,et al.  Generalized 2D principal component analysis , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[4]  Aleix M. Martínez,et al.  Where are linear feature extraction methods applicable? , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Jian-Huang Lai,et al.  GA-fisher: a new LDA-based face recognition algorithm with selection of principal components , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Takeo Kanade,et al.  Multimodal oriented discriminant analysis , 2005, ICML.

[7]  Huilin Xiong,et al.  Two-dimensional FLD for face recognition , 2005, Pattern Recognit..

[8]  Jian Yang,et al.  Two-dimensional discriminant transform for face recognition , 2005, Pattern Recognit..

[9]  Lei Wang,et al.  A framework of 2D Fisher discriminant analysis: application to face recognition with small number of training samples , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  Nitesh V. Chawla,et al.  Random subspaces and subsampling for 2-D face recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Jing Peng,et al.  Discriminant Analysis: A Least Squares Approximation View , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[12]  Dong Xu,et al.  Discriminant analysis with tensor representation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  J. Kittler,et al.  Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Yuxiao Hu,et al.  Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Jeffery R. Price,et al.  Face recognition using direct, weighted linear discriminant analysis and modular subspaces , 2005, Pattern Recognit..

[16]  Konstantinos N. Plataniotis,et al.  Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition , 2005, Pattern Recognit. Lett..

[17]  Dong Xu,et al.  Parallel Image Matrix Compression for Face Recognition , 2005, 11th International Multimedia Modelling Conference.

[18]  Jieping Ye,et al.  Two-Dimensional Linear Discriminant Analysis , 2004, NIPS.

[19]  Jieping Ye,et al.  An optimization criterion for generalized discriminant analysis on undersampled problems , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Jieping Ye.  Generalized Low Rank Approximations of Matrices , 2004, Machine Learning.

[21]  Robert P. W. Duin,et al.  Linear dimensionality reduction via a heteroscedastic extension of LDA: the Chernoff criterion , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Jieping Ye,et al.  LDA/QR: an efficient and effective dimension reduction algorithm and its theoretical foundation , 2004, Pattern Recognit..

[23]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Pong C. Yuen,et al.  Regularized discriminant analysis and its application to face recognition , 2003, Pattern Recognit..

[25]  Jian Yang,et al.  Why can LDA be performed in PCA transformed space? , 2003, Pattern Recognit..

[26]  Hyun-Chul Kim,et al.  Face recognition using LDA mixture model , 2002, Object recognition supported by user interaction for service robots.

[27]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

[28]  Jian Yang,et al.  From image vector to matrix: a straightforward image projection technique - IMPCA vs. PCA , 2002, Pattern Recognit..

[29]  Hanqing Lu,et al.  Solving the small sample size problem of LDA , 2002, Object recognition supported by user interaction for service robots.

[30]  Jian-Huang Lai,et al.  Face representation using independent component analysis , 2002, Pattern Recognit..

[31]  Stan Z. Li,et al.  Learning spatially localized, parts-based representation , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[32]  Hua Yu,et al.  A direct LDA algorithm for high-dimensional data - with application to face recognition , 2001, Pattern Recognit..

[33]  E. Oja,et al.  Independent Component Analysis , 2001 .

[34]  A. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Alex Pentland,et al.  Bayesian face recognition , 2000, Pattern Recognit..

[36]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[37]  Ja-Chen Lin,et al.  A new LDA-based face recognition system which can solve the small sample size problem , 1998, Pattern Recognit..

[38]  Chengjun Liu,et al.  Enhanced Fisher linear discriminant models for face recognition , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[39]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[40]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[42]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[43]  Jing-Yu Yang,et al.  Algebraic feature extraction for image recognition based on an optimal discriminant criterion , 1993, Pattern Recognit..

[44]  L. Duchene,et al.  An Optimal Transformation for Discriminant and Principal Component Analysis , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[45]  Zhi-Hua Zhou,et al.  Diagonal principal component analysis for face recognition , 2006, Pattern Recognit..

[46]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[47]  Hakan Cevikalp,et al.  Discriminative common vectors for face recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[48]  E. R. Davies,et al.  Statistical Pattern Recognition , 2005 .

[49]  Jian Yang,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Jing-Yu Yang,et al.  Face recognition based on the uncorrelated discriminant transformation , 2001, Pattern Recognit..

[51]  R. Chellappa,et al.  Subspace Linear Discriminant Analysis for Face Recognition , 1999 .