This paper presents a new algorithm for the independent components analysis (ICA) problem based on efficient entropy estimates. Like many previous methods, this algorithm directly minimizes the measure of departure from independence according to the estimated Kullback-Leibler divergence between the joint distribution and the product of the marginal distributions. We pair this approach with efficient entropy estimators from the statistics literature. In particular, the entropy estimator we use is consistent and exhibits rapid convergence. The algorithm based on this estimator is simple, computationally efficient, intuitively appealing, and outperforms other well known algorithms. In addition, the estimator's relative insensitivity to outliers translates into superior performance by our ICA algorithm on outlier tests. We present favorable comparisons to the Kernel ICA, FAST-ICA, JADE, and extended Infomax algorithms in extensive simulations.
Title
Independent Components Analysis by Direct Entropy Minimization
Published
Computer Science Division, University of California at Berkeley, Berkeley, California, January 2003
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
CSD-03-1221
Type
Text
Extent
26 p
Archive
The Engineering Library
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