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Abstract: This paper presents a new algorithm for the independent components analysis (ICA) problem based on efficient spacings estimates of entropy. Like many previous methods, we minimize a standard measure of the departure from independence, the estimated Kullback-Leibler divergence between a joint distribution and the product of its marginals. To do this, we use a consistent and rapidly converging entropy estimator due to Vasicek. The resulting algorithm is simple, computationally efficient,... (Update)
Context of citations to this paper: More
...closed form entropy calculation from the parametric density (e.g. Gaussian. Empirical estimators based on order statistics and spacings [91] o#er a direct path for deriving entropies from a set of data. Better methods are needed for the proper interpretation of the...
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BibTeX entry: (Update)
E. G. Miller and J. W. Fisher, "ICA using spacing estimates of entropy," in Proc. Fourth International Symposium on Independent Component Analysis and Blind Signal Separation, Nara, Japan, Apr. 2003, pp. 1047--1052. http://citeseer.comp.nus.edu.sg/683503.html More
@misc{ miller03ica,
author = "E. Miller and J. Fisher",
title = "ICA using spacing estimates of entropy",
text = "E. G. Miller and J. W. Fisher, ICA using spacing estimates of entropy,
in Proc. Fourth International Symposium on Independent Component Analysis
and Blind Signal Separation, Nara, Japan, Apr. 2003, pp. 1047--1052.",
year = "2003",
url = "citeseer.comp.nus.edu.sg/683503.html" }
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