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An Iterative Algorithm for Robust Kernel Principal Component Analysis

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Title: An Iterative Algorithm for Robust Kernel Principal Component Analysis
Author(s): Huang, Hsin-Hsiung; Yeh, Yi-Ren
Abstract: We introduce a technique to improve iterative kernel principal component analysis (KPCA) robust to outliers due to undesirable artifacts such as noises, alignment errors, or occlusion. The proposed iterative robust KPCA (rKPCA) links the iterative updating and robust estimation of principal directions. It inherits good properties from these two ideas for reducing the time complexity, space complexity, and the influence of these outliers on estimating the principal directions. In the asymptotic stability analysis, we also show that our iterative rKPCA converges to the weighted kernel principal kernel components from the batch rKPCA. Experimental results are presented to confirm that our iterative rKPCA achieves the robustness as well as time saving better than batch KPCA.
Issue Date: 2011-11
Publisher: Elsevier
Citation Info: Huang, H. H. & Yeh, Y. R. 2011. An iterative algorithm for robust kernel principal component analysis. Neurocomputing, 74(18): 3921-3930. DOI: 10.1016/j.neucom.2011.08.008
Type: Article
Description: NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing , Vol 74, Issue 18, (NOV 2011) DOI: 10.1016/j.neucom.2011.08.008
URI: http://hdl.handle.net/10027/8300
ISSN: 0925-2312
Date Available in INDIGO: 2012-04-30
 

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