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Nonparametric divergence estimators for Independent Subspace Analysis

Póczos, B; Szabo, Z; Schneider, J; (2011) Nonparametric divergence estimators for Independent Subspace Analysis. In: (Proceedings) European Signal Processing Conference (EUSIPCO) - Special Session on Dependent Component Analysis. (pp. 1849 - 1853). Green open access

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Abstract

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 ''naive'' 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.

Type: Proceedings paper
Title: Nonparametric divergence estimators for Independent Subspace Analysis
Event: European Signal Processing Conference (EUSIPCO) - Special Session on Dependent Component Analysis
Location: Barcelona, Spain
Dates: 2011-08-29 - 2011-09-02
Open access status: An open access version is available from UCL Discovery
Publisher version: http://www.eurasip.org/Proceedings/Eusipco/Eusipco...
Language: English
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: https://discovery.ucl.ac.uk/id/eprint/1433151
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