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An efficient kernel discriminant analysis method

✍ Scribed by Juwei Lu; K.N. Plataniotis; A.N. Venetsanopoulos; Jie Wang

Publisher: Elsevier Science
Year: 2005
Tongue: English
Weight: 167 KB
Volume: 38
Category: Article
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2005.02.005

No coin nor oath required. For personal study only.

✦ Synopsis

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.

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