206 search results - page 5 / 42 » PCA in Autocorrelation Space |
PKDD
2010
Springer
13 years 6 months ago
2010
Springer
Abstract. Principal component analysis (PCA) and its dual—principal coordinate analysis (PCO)—are widely applied to unsupervised dimensionality reduction. In this paper, we sho...
FGR
2000
IEEE
13 years 12 months ago
2000
IEEE
Low-dimensional representations of sensory signals are key to solving many of the computational problems encountered in high-level vision. Principal Component Analysis (PCA) has b...
ICIP
2008
IEEE
14 years 9 months ago
2008
IEEE
Kernel PCA has received a lot of attention over the past years and showed usefull for many image processing problems. In this paper we analyse the issue of normalization in Kernel...
NIPS
1997
13 years 9 months ago
1997
I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large col...
ICCV
2007
IEEE
14 years 1 months ago
2007
IEEE
Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPC...