Preprint Article Version 1 NOT YET PEER-REVIEWED

Point Information Gain and Multidimensional Data Analysis

  1. Institute of Complex Systems, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, FFPW, University of South Bohemia in České Budějovice, Zámek 136, Nové Hrady 37333, Czech Republic
  2. Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Břehová 7, Prague 15519, Czech Republic
Version 1 : Received: 16 October 2016 / Approved: 17 October 2016 / Online: 17 October 2016 (11:35:13 CEST)

A peer-reviewed article of this Preprint also exists.

Rychtáriková, R.; Korbel, J.; Macháček, P.; Císař, P.; Urban, J.; Štys, D. Point Information Gain and Multidimensional Data Analysis. Entropy 2016, 18, 372. Rychtáriková, R.; Korbel, J.; Macháček, P.; Císař, P.; Urban, J.; Štys, D. Point Information Gain and Multidimensional Data Analysis. Entropy 2016, 18, 372.

Journal reference: Entropy 2016, 18, 372
DOI: 10.3390/e18100372

Abstract

We generalize the point information gain (PIG) and derived quantities, i.e., point information gain entropy (PIE) and point information gain entropy density (PIED), for the case of the Rényi entropy and simulate the behavior of PIG for typical distributions. We also use these methods for the analysis of multidimensional datasets. We demonstrate the main properties of PIE/PIED spectra for the real data on the example of several images, and discuss further possible utilizations in other fields of data processing.

Subject Areas

point information gain; Rényi entropy; data processing

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