Chebyshev Center Computation on Probability Simplex With alpha-Divergence Measure


CANDAN Ç.

IEEE SIGNAL PROCESSING LETTERS, vol.27, pp.1515-1519, 2020 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 27
  • Publication Date: 2020
  • Doi Number: 10.1109/lsp.2020.3018661
  • Journal Name: IEEE SIGNAL PROCESSING LETTERS
  • Journal Indexes: Science Citation Index Expanded, Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.1515-1519
  • Keywords: Arimoto-Blahut algorithm, minimax-redundancy, redundancy-capacity theorem, alpha-divergence, Renyi-divergence, information fusion, error exponent calculation, ALGORITHM, CAPACITY, EXPONENT

Abstract

Chebyshev center computation problem, i.e. finding the point which is at minimum distance to a set of given points, on the probability simplex with alpha-divergence distancemeasure is studied. The proposed solution generalizes the ArimotoBlahut (AB) algorithm utilizing Kullback-Leibler divergence to alpha-divergence, and reduces to the AB method as a. 1. Similar to the AB algorithm, themethod is an ascent method with a guarantee onthe objective value (alpha-mutual information or Chebyshev radius) improvement at every iteration. A practical application area for the method is the fusion of probability mass functions lacking a joint probability description. Another application area is the error exponent calculation.