89 search results - page 3 / 18 » Fast Independent Component Analysis in Kernel Feature Spaces |
ICANN
1997
Springer
13 years 9 months ago
1997
Springer
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can e ciently compute principal comp...
ICA
2007
Springer
13 years 11 months ago
2007
Springer
We present a robust algorithm for independent component analysis that uses the sum of marginal quadratic negentropies as a dependence measure. It can handle arbitrary source densit...
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...
NIPS
2008
13 years 7 months ago
2008
Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion. In this paper, we extend this c...
NPL
2006
13 years 5 months ago
2006
This paper presents a novel dimension reduction algorithm for kernel based classification. In the feature space, the proposed algorithm maximizes the ratio of the squared between-c...