Robust kernel principal component analysis

Authors:
Su-Yun Huang;Yi-Ren Yeh;Shinto Eguchi
Affiliations:
-;-;-
Venue:
Neural Computation
Year:
2009

Citing 6
Cited 3

Nonlinear component analysis as a kernel eigenvalue problem

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Kernel principal component analysis

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Robust Principal Component Analysis with Adaptive Selection for Tuning Parameters

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Reduced Support Vector Machines: A Statistical Theory

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An iterative algorithm for robust kernel principal component analysis

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A rank-one update method for least squares linear discriminant analysis with concept drift

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Asymptotic error bounds for kernel-based Nyström low-rank approximation matrices

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Abstract

This letter discusses the robustness issue of kernel principal component analysis. A class of new robust procedures is proposed based on eigenvalue decomposition of weighted covariance. The proposed procedures will place less weight on deviant patterns and thus be more resistant to data contamination and model deviation. Theoretical influence functions are derived, and numerical examples are presented as well. Both theoretical and numerical results indicate that the proposed robust method outperforms the conventional approach in the sense of being less sensitive to outliers. Our robust method and results also apply to functional principal component analysis.