Journal
PATTERN RECOGNITION
Volume 50, Issue -, Pages 45-55Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2015.08.021
Keywords
Kernel discriminant analysis; Computational complexity; Lagrange method; Regularization; Pattern recognition
Funding
- National Research Foundation of Korea
- Ministry of Science, Information and Communications Technology, and Future Planning [NRF-2015R1A2A1A01005868]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available