Class-Incremental Generalized Discriminant Analysis
Neural Computation
An efficient algorithm for generalized discriminant analysis using incomplete Cholesky decomposition
Pattern Recognition Letters
Feature extraction using constrained maximum variance mapping
Pattern Recognition
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An MRF-based kernel method for nonlinear feature extraction
Image and Vision Computing
A rank-one update algorithm for fast solving kernel Foley-Sammon optimal discriminant vectors
IEEE Transactions on Neural Networks
Face recognition using kernel uncorrelated discriminant analysis
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
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A new nonlinear feature extraction method called kernel Foley-Sammon optimal discriminant vectors (KFSODVs) is presented in this paper. This new method extends the well-known Foley-Sammon optimal discriminant vectors (FSODVs) from linear domain to a nonlinear domain via the kernel trick that has been used in support vector machine (SVM) and other commonly used kernel-based learning algorithms. The proposed method also provides an effective technique to solve the so-called small sample size (SSS) problem which exists in many classification problems such as face recognition. We give the derivation of KFSODV and conduct experiments on both simulated and real data sets to confirm that the KFSODV method is superior to the previous commonly used kernel-based learning algorithms in terms of the performance of discrimination.