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arXiv · 1008.1398

Semi-Supervised Kernel PCA

Christian Walder·Ricardo Henao·Morten Mørup·Lars Kai Hansen
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Abstract

We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA, penalises within class variances similar to Fisher discriminant analysis. The second, LSKPCA is a hybrid of least squares regression and kernel PCA. The final LR-KPCA is an iteratively reweighted version of the previous which achieves a sigmoid loss function on the labeled points. We provide a theoretical risk bound as well as illustrative experiments on real and toy data sets.

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Christian Walder, Ricardo Henao, Morten Mørup, Lars Kai Hansen. 2010-08-08. Semi-Supervised Kernel PCA. https://arxiv.org/abs/1008.1398

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