Szabo, Z;
(2014)
Information Theoretical Estimators Toolbox.
Journal of Machine Learning Research
, 15
283 - 287.
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Abstract
We present ITE (information theoretical estimators) a free and open source, multi-platform, Matlab/Octave toolbox that is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities, and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems. ITE also includes a prototype application in a central problem class of signal processing, independent subspace analysis and its extensions.
| Type: | Article |
|---|---|
| Title: | Information Theoretical Estimators Toolbox |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://jmlr.org/papers/v15/szabo14a.html |
| Language: | English |
| Additional information: | © 2014 Szabo. Reproduced here by permission of JMLR. |
| Keywords: | entropy estimation, mutual information estimation, association estimation, divergence estimation, distribution kernel estimation, independent subspace analysis and its extensions, modularity, Matlab/Octave, multi-platform, GNU GPLv3 (>=) |
| 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/1433095 |
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