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, and J-distance.

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:

• Two Shannon entropy estimators based on the distance (KL divergence) from the uniform/Gaussian distributions: added.

• Shannon entropy estimator based on Voronoi regions: 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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