Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
The Journal of Machine Learning Research
Generalized Low Rank Approximations of Matrices
Machine Learning
An assembled matrix distance metric for 2DPCA-based image recognition
Pattern Recognition Letters
(2D)2LDA: An efficient approach for face recognition
Pattern Recognition
Volume measure in 2DPCA-based face recognition
Pattern Recognition Letters
Rapid and brief communication: Two-dimensional FLD for face recognition
Pattern Recognition
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
An optimization criterion for generalized discriminant analysis on undersampled problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A complete fuzzy discriminant analysis approach for face recognition
Applied Soft Computing
Computers & Mathematics with Applications
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2DLDA and its variants have attracted much attention from researchers recently due to the advantages over the singularity problem and the computational cost. In this paper, we further analyze the 2DLDA method and derive the upper bound of its criterion. Based on this upper bound, we show that the discriminant power of two-dimensional discriminant analysis is not stronger than that of LDA under the assumption that the same dimensionality is considered. In experimental parts, on one hand, we confirm the validity of our claim and show the matrix-based methods are not always better than vector-based methods in the small sample size problem; on the other hand, we compare several distance measures when the feature matrices and feature vectors are applied. The matlab codes used in this paper are available at http://www.mathworks.com/matlabcentral/fileexchange/loadCategory.do?objectType=category&objectId=127&objectName=Application.