TY - RPRT
T1 - Semi-Supervised Kernel PCA
AU - Walder, Christian
AU - Henao, Ricardo
AU - Mørup, Morten
AU - Hansen, Lars Kai
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
M3 - Report
T3 - IMM-Technical Report-2010-10
BT - Semi-Supervised Kernel PCA
PB - Technical University of Denmark, DTU Informatics, Building 321
CY - Kgs. Lyngby, Denmark
ER -