An Optimal Transformation for Discriminant and Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Fractional-Step Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
A Kernel Fractional-Step Nonlinear Discriminant Analysis for Pattern Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Improved-LDA based face recognition using both facial global and local information
Pattern Recognition Letters
Face recognition using a kernel fractional-step discriminant analysis algorithm
Pattern Recognition
A comparison of generalized linear discriminant analysis algorithms
Pattern Recognition
An Optimal Set of Discriminant Vectors
IEEE Transactions on Computers
Kernel-based improved discriminant analysis and its application to face recognition
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Pattern Recognition and Information Processing Using Neural Networks;Guest Editors: Fuchun Sun,Ying Tan,Cong Wang
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
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Kernel discriminant analysis (KDA) is a widely used approach in feature extraction problems. However, for high-dimensional multi-class tasks, such as faces recognition, traditional KDA algorithms have a limitation that the Fisher criterion is non-optimal with respect to classification rate. Moreover, they suffer from the small sample size problem. This paper presents two variants of KDA called based on QR decomposition weighted kernel discriminant analysis (WKDA/QR), which can effectively deal with the above two problems, and based on singular value decomposition weighted kernel discriminant analysis (WKDA/SVD). Since the QR decomposition on a small size matrix is adopted, the superiority of the proposed method is its computational efficiency and can avoid the singularity problem. In addition, we compare WKDA/QR with WKDA/SVD under the parameters of weighted function and kernel function. Experimental results on face recognition show that the WKDA/QR and WKDA/SVD are more effective than KDA, and WKDA/QR is more effective and feasible than WKDA/SVD.