299 search results - page 2 / 60 » Multiple kernel learning and feature space denoising |
WSCG
2004
13 years 6 months ago
2004
Principal Component Analysis (PCA) is a basis transformation to diagonalize an estimate of the covariance matrix of input data and, the new coordinates in the Eigenvector basis ar...
ICPR
2006
IEEE
14 years 6 months ago
2006
IEEE
This paper presents a novel alternative approach, namely weakly supervised learning (WSL), to learn the pre-image of a feature vector in the feature space induced by a kernel. It ...
23
Voted
ICPR
2006
IEEE
2006
IEEE
Regularized Locality Preserving Learning of Pre-Image Problem in Kernel Principal Component Analysis
13 years 11 months ago
In this paper, we address the pre-image problem in kernel principal component analysis (KPCA). The preimage problem finds a pattern as the pre-image of a feature vector defined in...
MCS
2010
Springer
14 years 4 days ago
2010
Springer
Abstract. In this paper we present a novel approach to combining multiple kernels where the kernels are computed from different information channels. In contrast to traditional me...
CVPR
2011
IEEE
13 years 22 days ago
2011
IEEE
Kernel methods have been popular over the last decade to solve many computer vision, statistics and machine learning problems. An important, both theoretically and practically, op...