Rapid and brief communication: An efficient kernel discriminant analysis method

Authors:
Juwei Lu;K. N. Plataniotis;A. N. Venetsanopoulos;Jie Wang
Affiliations:
Multimedia Laboratory, The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Ontario, Canada M5S 3G4;Multimedia Laboratory, The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Ontario, Canada M5S 3G4;Multimedia Laboratory, The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Ontario, Canada M5S 3G4;Multimedia Laboratory, The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Ontario, Canada M5S 3G4
Venue:
Pattern Recognition
Year:
2005

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Abstract

Small sample size and high computational complexity are two major problems encountered when traditional kernel discriminant analysis methods are applied to high-dimensional pattern classification tasks such as face recognition. In this paper, we introduce a new kernel discriminant learning method, which is able to effectively address the two problems by using regularization and subspace decomposition techniques. Experiments performed on real face databases indicate that the proposed method outperforms, in terms of classification accuracy, existing kernel methods, such as kernel principal component analysis and kernel linear discriminant analysis, at a significantly reduced computational cost.