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
Feature extraction via generalized uncorrelated linear discriminant analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
SIAM Journal on Matrix Analysis and Applications
The Journal of Machine Learning Research
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Training support vector machines using greedy stagewise algorithm
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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
Constructing sparse KFDA using pre-image reconstruction
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
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Kernel fisher discriminant analysis (KFDA) has received extensive study in recent years as a dimensionality reduction technique. KFDA always encounters an intrinsic singularity of scatter matrices in the feature space, namely ‘small sample size' (SSS) problem. Several novel methods have been proposed to cope with this problem. In this paper, kernel uncorrelated discriminant analysis (KUDA) is proposed, which not only can bear on the SSS problem but also extract uncorrelated features, a desirable property for many applications. And then, we have conducted a comparative study on the application of KUDA and other variants of KFDA in radar target recognition problem. The experimental results indicate the effectiveness of KUDA and illustrate the utility of KFDA on the problem.