Tangent Hyperplane Kernel Principal Component Analysis for Denoising

J. K. Im, D. W. Apley, G. Runger

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Kernel principal component analysis (KPCA) is a method widely used for denoising multivariate data. Using geometric arguments, we investigate why a projection operation inherent to all existing KPCA denoising algorithms can sometimes cause very poor denoising. Based on this, we propose a modification to the projection operation that remedies this problem and can be incorporated into any of the existing KPCA algorithms. Using toy examples and real datasets, we show that the proposed algorithm can substantially improve denoising performance and is more robust to misspecification of an important tuning parameter.
Original languageEnglish
Pages (from-to)644-656
JournalIEEE Transactions on Neural Networks
StatePublished - Apr 2012


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