PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Nonparametric divergence estimators for independent subspace analysis
Barnabas Poczos, Zoltan Szabo and Jeff Schneider
In: 19th European Signal Processing Conference (EUSIPCO) -- Special Session on Dependent Component Analysis, 29 Aug - 02 Sep 2011, Barcelona, Spain.

There is a more recent version of this eprint available. Click here to view it.


In this paper we propose new nonparametric Rényi, Tsallis, and L2 divergence estimators and demonstrate their applicability to mutual information estimation and independent subspace analysis. Given two independent and identically distributed samples, a ''naïve'' divergence estimation approach would simply estimate the underlying densities, and plug these densities into the corresponding integral formulae. In contrast, our estimators avoid the need to consistently estimate these densities, and still they can lead to consistent estimations. Numerical experiments illustrate the efficiency of the algorithms.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Additional Information:
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8382
Deposited By:Zoltan Szabo
Deposited On:01 December 2011

Available Versions of this Item