2010/08/08 by Christian Walder, Walder, Christian, Ricardo Henao +5
Computer Science · #Blind Source Separation Techniques #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Neural Networks and Applications #cs.LG
paper · pdf · doi:10.48550/arxiv.1008.1398
arxiv created 2010/08/08 · openalex publication_date 2010/08/08 · arxiv updated 2010/08/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.