Modified Kernel PCA Applied To Classification Problem


The KIPS Transactions:PartB , Vol. 10, No. 3, pp. 243-248, Jun. 2003
10.3745/KIPSTB.2003.10.3.243,   PDF Download:

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.


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Cite this article
[IEEE Style]
B. J. Kim, J. Y. Sim, C. H. Hwang, I. K. Kim, "Modified Kernel PCA Applied To Classification Problem," The KIPS Transactions:PartB , vol. 10, no. 3, pp. 243-248, 2003. DOI: 10.3745/KIPSTB.2003.10.3.243.

[ACM Style]
Byung Joo Kim, Joo Yong Sim, Chang Ha Hwang, and Il Kon Kim. 2003. Modified Kernel PCA Applied To Classification Problem. The KIPS Transactions:PartB , 10, 3, (2003), 243-248. DOI: 10.3745/KIPSTB.2003.10.3.243.