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An Improved Algorithm for Kernel Principal Component Analysis

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Abstract

Kernel principal component analysis (KPCA), introduced by Schölkopf et al., is a nonlinear generalization of the popular principal component analysis (PCA) via the kernel trick. KPCA has shown to be a very powerful approach of extracting nonlinear features for classification and regression applications. However, the standard KPCA algorithm (Schölkopf et al., 1998, Neural Computation 10, 1299–1319) may suffer from computational problem for large scale data set. To overcome these drawbacks, we propose an efficient training algorithm in this paper, and show that this approach is of much more computational efficiency compared to the previous ones for KPCA.

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Abbreviations

EM:

Expectation Maximization

KPCA:

Kernel Principal Component Analysis

PCA:

Principal Component Analysis

Reference

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Authors and Affiliations

  1. Research Center for Learning Science, Southeast University, Nanjing, Jiangsu, 210096, People’s Republic of China

    Wenming Zheng

  2. Engineering Research Center of Information Processing and Application, Southeast University, Nanjing, Jiangsu, 210096, People’s Republic of China

    Cairong Zou & Li Zhao

Authors
  1. Wenming Zheng
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  2. Cairong Zou
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  3. Li Zhao
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Corresponding author

Correspondence to Wenming Zheng.

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Zheng, W., Zou, C. & Zhao, L. An Improved Algorithm for Kernel Principal Component Analysis. Neural Process Lett 22, 49–56 (2005). https://doi.org/10.1007/s11063-004-0036-x

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  • DOI: https://doi.org/10.1007/s11063-004-0036-x

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