Semi-Supervised Kernel PCA
Machine Learning
2010-08-10 v1
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.
Cite
@article{arxiv.1008.1398,
title = {Semi-Supervised Kernel PCA},
author = {Christian Walder and Ricardo Henao and Morten Mørup and Lars Kai Hansen},
journal= {arXiv preprint arXiv:1008.1398},
year = {2010}
}