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NIPS
2007
13 years 10 months ago
An online Hebbian learning rule that performs Independent Component Analysis
Independent component analysis (ICA) is a powerful method to decouple signals. Most of the algorithms performing ICA do not consider the temporal correlations of the signal, but o...
Claudia Clopath, André Longtin, Wulfram Ger...
IJHPCA
2008
104views more  IJHPCA 2008»
13 years 9 months ago
Low-Complexity Principal Component Analysis for Hyperspectral Image Compression
Principal component analysis (PCA) is an effective tool for spectral decorrelation of hyperspectral imagery, and PCA-based spectral transforms have been employed successfully in co...
Qian Du, James E. Fowler
CSDA
2007
76views more  CSDA 2007»
13 years 9 months ago
Independent component analysis based on symmetrised scatter matrices
A new method for separating the mixtures of independent sources has been proposed recently in [8]. This method is based on two scatter matrices with the so called independence pro...
Sara Taskinen, S. Sirkiä, Hannu Oja
AI
2005
Springer
13 years 9 months ago
Fast Protein Superfamily Classification Using Principal Component Null Space Analysis
Abstract. The protein family classification problem, which consists of determining the family memberships of given unknown protein sequences, is very important for a biologist for ...
Leon French, Alioune Ngom, Luis Rueda
JMLR
2002
160views more  JMLR 2002»
13 years 8 months ago
Kernel Independent Component Analysis
We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On th...
Francis R. Bach, Michael I. Jordan