Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Subclass Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A kernel optimization method based on the localized kernel Fisher criterion
Pattern Recognition
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
An empirical comparison of Kernel-based and dissimilarity-based feature spaces
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
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In order to overcome the restricts of linear discriminant analysis (LDA), such as multivariate Normal distributed classes with equal covariance matrix but different means and the single-cluster structure in each class, subclass discriminant analysis (SDA) is proposed recently. In this paper the kernel SDA is presented, called KSDA. Moreover, we reformulate SDA so as to avoid the complicated derivation in the feature space. The encouraging experimental results on eight UCI data sets demonstrate the efficiency of our method.