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).
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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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