Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix
Pattern Recognition Letters
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
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In this paper, we establish a dual-objective optimization (DOO) model for discriminating feature extraction, in the sense that the optimizations of between-class scatter and within-class scatter are taken into investigation separately rather than simultaneously through a quotient like Fisher criterion. Based on the various solutions of the proposed model, we outline the optimization strategy of null space of within-class scatter matrix and the framework of complex PCA for classification purpose. We test the performance of the proposed algorithms on the ORL and Yale face databases. The experimental results show that the proposed algorithms are effective. Particularly, the complex PCA enhanced by complex LDA appears to be the best among the considered algorithms in terms of recognition performance and is robust against noises.