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0899-7667
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1530-888X
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Neural Computation

August 2010, Vol. 22, No. 8, Pages 2208-2227
(doi: 10.1162/neco.2010.02-09-972)
© 2010 Massachusetts Institute of Technology
Sample-Spacings-Based Density and Entropy Estimators for Spherically Invariant Multidimensional Data
Article PDF (1.47 MB)
Abstract

While the sample-spacings-based density estimation method is simple and efficient, its applicability has been restricted to one-dimensional data. In this letter, the method is generalized such that it can be extended to multiple dimensions in certain circumstances. As a consequence, a multidimensional entropy estimator of spherically invariant continuous random variables is derived. Partial bias of the estimator is analyzed, and the estimator is further used to derive a nonparametric objective function for frequency-domain independent component analysis. The robustness and the effectiveness of the objective function are demonstrated with simulation results.