1. Zoltán Szabó
  2. ITE



ITE (Information Theoretical Estimators)

ITE is capable of estimating many different variants of entropy, mutual information and divergence measures. Thanks to its highly modular design, ITE supports additionally

  • the combinations of the estimation techniques,
  • the easy construction and embedding of novel information theoretical estimators, and
  • their immediate application in information theoretical optimization problems.

ITE is

  • written in Matlab/Octave,
  • multi-platform (tested extensively on Windows and Linux),
  • free and open source (released under the GNU GPLv3(>=) license).

ITE can estimate Shannon-, Rényi entropy; generalized variance, kernel canonical correlation analysis, kernel generalized variance, Hilbert-Schmidt independence criterion, Shannon-, L2-, Rényi-, Tsallis mutual information, copula-based kernel dependency; complex variants of entropy and mutual information; L2-, Rényi, Tsallis divergence, 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 systems, as well as to systems with nonparametric source dynamics.