921 search results - page 32 / 185 » Kernel Principal Component Analysis |
TNN
2008
13 years 7 months ago
2008
Abstract--Large-margin methods, such as support vector machines (SVMs), have been very successful in classification problems. Recently, maximum margin discriminant analysis (MMDA) ...
NIPS
2003
13 years 9 months ago
2003
Eigenvoice speaker adaptation has been shown to be effective when only a small amount of adaptation data is available. At the heart of the method is principal component analysis (...
ICA
2004
Springer
2004
Springer
Using Kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method
14 years 1 months ago
The variational Bayesian nonlinear blind source separation method introduced by Lappalainen and Honkela in 2000 is initialised with linear principal component analysis (PCA). Becau...
PRL
2008
13 years 7 months ago
2008
We introduce a semi-supervised learning estimator which tends to the first kernel principal component as the number of labeled points vanishes. We show application of the proposed...
ICIP
2005
IEEE
14 years 9 months ago
2005
IEEE
Robustly tracking moving objects in video sequences is one of the key problems in computer vision. In this paper we introduce a computationally efficient nonlinear kernel learning...