Project details for Information Theoretical Estimators

Screenshot Information Theoretical Estimators 0.21

by szzoli - November 25, 2012, 20:56:32 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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Description:

ITE can estimate Shannon-, Rényi-, Tsallis entropy; generalized variance, kernel canonical correlation analysis, kernel generalized variance, Hilbert-Schmidt independence criterion, Shannon-, L2-, Rényi-, Tsallis mutual information, copula-based kernel dependency, multivariate version of Hoeffding's Phi; complex variants of entropy and mutual information; L2-, Rényi-, Tsallis-, Kullback-Leibler divergence; Hellinger-, Bhattacharyya distance, maximum mean discrepancy, J-distance and cross-entropy.

ITE offers solution methods for

  • Independent Subspace Analysis (ISA) and
  • its extensions to different linear-, controlled-, post nonlinear-, complex valued-, partially observed models, as well as to systems with nonparametric source dynamics.

ITE is

  • written in Matlab/Octave,
  • multi-platform (tested extensively on Windows and Linux),
  • free and open source (released under the GNU GPLv3(>=) license).
Changes to previous version:
  • Kullback-Leibler divergence estimator based on cross-entropy and entropy: added.

  • Cross-entropy estimation based on k-nearest neighbors: added.

  • Cross cost object type: added.

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Linux, Windows
Data Formats: Matlab, Octave
Tags: Entropy, Mutual Information, Divergence, Independent Subspace Analysis, Separation Principles, Independent Process Analysis
Archive: download here

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