A computationally efficient scheme for feature extraction with kernel discriminant analysis

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The kernel discriminant analysis (KDA), an extension of the linear discriminant analysis (LDA) and null space-based LDA into the kernel space, generally provides good pattern recognition (PR) performance for both small sample size (SSS) and non-SSS PR problems. Due to the eigen-decomposition technique adopted, however, the original scheme for the feature extraction with the KDA suffers from a high complexity burden. In this paper, we derive a transformation of the KDA into a linear equation problem, and propose a novel scheme for the feature extraction with the KDA. The proposed scheme is shown to provide us with a reduction of complexity without degradation of PR performance. In addition, to enhance the PR performance further, we address the incorporation of regularization into the proposed scheme.
Publisher
ELSEVIER SCI LTD
Issue Date
2016-02
Language
English
Article Type
Article
Citation

PATTERN RECOGNITION, v.50, pp.45 - 55

ISSN
0031-3203
DOI
10.1016/j.patcog.2015.08.021
URI
http://hdl.handle.net/10203/205094
Appears in Collection
EE-Journal Papers(저널논문)
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