期刊
PATTERN RECOGNITION
卷 50, 期 -, 页码 45-55出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2015.08.021
关键词
Kernel discriminant analysis; Computational complexity; Lagrange method; Regularization; Pattern recognition
资金
- National Research Foundation of Korea
- Ministry of Science, Information and Communications Technology, and Future Planning [NRF-2015R1A2A1A01005868]
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. (C) 2015 Elsevier Ltd. All rights reserved.
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