Project details for Information Theoretical Estimators

Screenshot Information Theoretical Estimators 0.34

by szzoli - March 22, 2013, 11:41:05 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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ITE can estimate

  • entropy: Shannon entropy, Rényi entropy, Tsallis entropy (Havrda and Charvát entropy), complex entropy,

  • mutual information: generalized variance, kernel canonical correlation analysis, kernel generalized variance, Hilbert-Schmidt independence criterion, Shannon mutual information, L2 mutual information, Rényi mutual information, Tsallis mutual information, copula-based kernel dependency, multivariate version of Hoeffding's Phi, complex mutual information, Cauchy-Schwartz quadratic mutual information, Euclidean distance based quadratic mutual information, distance covariance, distance correlation, approximate correntropy independence measure,

  • divergence: Kullback-Leibler divergence (relative entropy), L2 divergence, Rényi divergence, Tsallis divergence, Hellinger distance, Bhattacharyya distance, maximum mean discrepancy (kernel distance), J-distance (symmetrised Kullback-Leibler divergence), Cauchy-Schwartz divergence, Euclidean distance based divergence, energy distance (specially the Cramer-Von Mises distance),

  • association measures, including measures of concordance: multivariate extensions of Spearman's rho (Spearman's rank correlation coefficient, grade correlation coefficient), correntropy, centered correntropy, correntropy coefficient, correntropy induced metric, centered correntropy induced metric, multivariate extension of Blomqvist's beta (medial correlation coefficient), multivariate conditional version of Spearman's rho, lower/upper tail dependence via conditional Spearman's rho,

  • cross quantities: 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:

Jacobi optimization based ICA solution with general entropy/mutual information estimators: added. The method extends the SWICA scheme to general objectives.

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, Association Measure, Measure Of Concordance, Measure Of Independence, Nonpa
Archive: download here


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