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http://dx.doi.org/10.3745/KIPSTB.2003.10B.3.243

Modified Kernel PCA Applied To Classification Problem  

Kim, Byung-Joo (영산대학교 컴퓨터 정보공학부)
Sim, Joo-Yong (대구가톨릭대학교 정보통계학과)
Hwang, Chang-Ha (대구가톨릭대학교 정보통계학과)
Kim, Il-Kon (경북대학교 컴퓨터과학과)
Publication Information
The KIPS Transactions:PartB / v.10B, no.3, 2003 , pp. 243-248 More about this Journal
Abstract
An incremental kernel principal component analysis (IKPCA) is proposed for the nonlinear feature extraction from the data. The problem of batch kernel principal component analysis (KPCA) is that the computation becomes prohibitive when the data set is large. Another problem is that, in order to update the eigenvectors with another data, the whole eigenspace should be recomputed. IKPCA overcomes these problems by incrementally computing eigenspace model and empirical kernel map The IKPCA is more efficient in memory requirement than a batch KPCA and can be easily improved by re-learning the data. In our experiments we show that IKPCA is comparable in performance to a batch KPCA for the feature extraction and classification problem on nonlinear data set.
Keywords
Eigenspace Model; Kernel PCA; Empirical Kernel Map; Nonlinear Feature Extraction;
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