664 search results - page 24 / 133 » Kernel Independent Component Analysis |
ICPR
2004
IEEE
14 years 8 months ago
2004
IEEE
Statistical methods, such as independent component analysis, have been successful in learning local low-level features from natural image data. Here we extend these methods for le...
DAGSTUHL
2006
13 years 9 months ago
2006
CSDA
2010
13 years 7 months ago
2010
Kernel Principal Component Analysis extends linear PCA from a Euclidean space to any reproducing kernel Hilbert space. Robustness issues for Kernel PCA are studied. The sensitivit...
NIPS
2003
13 years 8 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...
ICML
2004
IEEE
14 years 8 months ago
2004
IEEE
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...