187 search results - page 10 / 38 » Nonlinear Component Analysis as a Kernel Eigenvalue Problem |
ICA
2004
Springer
2004
Springer
Using Kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method
15 years 2 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...
71
Voted
NIPS
2003
14 years 11 months ago
2003
We derive the limiting form of the eigenvalue spectrum for sample covariance matrices produced from non-isotropic data. For the analysis of standard PCA we study the case where th...
80
Voted
ICPR
2010
IEEE
14 years 11 months ago
2010
IEEE
Canonical correlation analysis (CCA) is a powerful tool for analyzing multi-dimensional paired data. However, CCA tends to perform poorly when the number of paired samples is limit...
PR
2008
14 years 9 months ago
2008
In this paper, we make a study on three Linear Discriminant Analysis (LDA) based methods: Regularized Discriminant Analysis (RDA), Discriminant Common Vectors (DCV) and Maximal Ma...
71
Voted
PR
2006
2006
Correspondence matching using kernel principal components analysis and label consistency constraints
14 years 9 months ago
This paper investigates spectral approaches to the problem of point pattern matching. We make two contributions. First, we consider rigid point-set alignment. Here we show how ker...