A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving the Small Sample Size Problem of LDA
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Face Recognition by Using Discriminative Common Vectors
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
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
Robust coding schemes for indexing and retrieval from large face databases
IEEE Transactions on Image Processing
Extracting the optimal dimensionality for local tensor discriminant analysis
Pattern Recognition
Single image subspace for face recognition
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Gabor feature-based fast neighborhood component analysis for face recognition
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Face recognition by using overlapping block discriminative common vectors
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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Discriminant common vectors (DCV), neighbourhood components analysis (NCA) and Laplacianfaces (LAP) are three recently proposed methods which can effectively learn linear projection matrices for dimensionality reduction in face recognition, where the dimension of the sample space is typically larger than the number of samples in the training set and consequently the so-called small sample size (SSS) problem exists. The three methods obtained their respective projection matrices based on different objective functions and all claimed to be superior to such methods as Principal component analysis (PCA) and PCA plus Linear discriminant analysis (PCA+LDA) in terms of classification accuracy. However, in literature, no comparative study is carried out among them. In this paper, we carry out a comparative study among them in face recognition (or generally in the SSS problem), and argue that the projection matrix yielded by DCV is the optimal solution to both NCA and LAP in terms of their respective objective functions, whereas neither NCA nor LAP may get their own optimal solutions. In addition, we show that DCV is more efficient than both NCA and LAP for both linear dimensionality reduction and subsequent classification in SSS problem. Finally, experiments are conducted on ORL, AR and YALE face databases to verify our arguments and to present some insights for future study.