156 search results - page 4 / 32 » Generalized Principal Component Analysis (GPCA) |
NIPS
2008
13 years 8 months ago
2008
Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to highe...
ICPR
2006
IEEE
2006
IEEE
Regularized Locality Preserving Learning of Pre-Image Problem in Kernel Principal Component Analysis
14 years 1 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...
ECML
2007
Springer
13 years 11 months ago
2007
Springer
Recently, a successful extension of Principal Component Analysis for structured input, such as sequences, trees, and graphs, has been proposed. This allows the embedding of discret...
ICPR
2006
IEEE
14 years 8 months ago
2006
IEEE
In this paper, a multilinear formulation of the popular Principal Component Analysis (PCA) is proposed, named as multilinear PCA (MPCA), where the input can be not only vectors, b...
ECCV
2002
Springer
14 years 9 months ago
2002
Springer
Abstract. Principal Component Analysis (PCA) is one of the most popular techniques for dimensionality reduction of multivariate data points with application areas covering many bra...