Title :
Complexity reduction of kernel discriminant analysis
Author :
Hou, Yuxi ; Min, Hwang-Ki ; Lee, Seungwon ; Yoon, Seokho ; Lee, Seong Ro ; Song, Iickho
Author_Institution :
Dept. of Electr. Eng., Korea Adv. Inst. of Sci. & Technol., Daejeon, South Korea
Abstract :
As an extension of the linear discriminant analysis (LDA), the kernel discriminant analysis (KDA) generally results in good pattern recognition performance for both small sample size (SSS) and non-SSS problems. Yet, the original scheme based on the eigen-decomposition technique suffers from a complexity burden. In this paper, by transforming the problem of finding the feature extractor (FE) of the KDA into a linear equation problem, reduction of the complexity is accomplished via a novel scheme for the FE of the KDA.
Keywords :
computational complexity; eigenvalues and eigenfunctions; feature extraction; FE; KDA; LDA; complexity reduction; eigen-decomposition technique; feature extractor; kernel discriminant analysis; linear discriminant analysis; linear equation problem; nonSSS problems; pattern recognition performance; small sample size problem; Indexes; feature extraction; kernel discriminant analysis; small sample size;
Conference_Titel :
Information Sciences and Systems (CISS), 2012 46th Annual Conference on
Conference_Location :
Princeton, NJ
Print_ISBN :
978-1-4673-3139-5
Electronic_ISBN :
978-1-4673-3138-8
DOI :
10.1109/CISS.2012.6310782
