Model selection for Gaussian kernel PCA denoising

Kasper Winther Jørgensen, Lars Kai Hansen

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    We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR) of the denoised data.
    Original languageEnglish
    JournalI E E E Transactions on Neural Networks and Learning Systems
    Issue number1
    Pages (from-to)163-168
    Publication statusPublished - 2012


    • Kernel principal component analysis
    • Denoising
    • Parallel analysis
    • Model selection

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