Information-bottleneck Based on the Jensen-shannon Divergence with Applications to Pairwise Clustering

Jacob Goldberger, Yaniv Opochinsky

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The information-bottleneck (IB) principle is defined in terms of mutual information. This study defines mutual information between two random variables using the Jensen-Shannon (JS) divergence instead of the standard definition which is based on the Kullback-Leibler (KL) divergence. We reformulate the information-bottleneck principle using the proposed mutual information and apply it to the problem of pairwise clustering. We show that applying IB to clustering tasks using JS divergences instead of KL yields improved results. This indicates that JS-based mutual information has an expressive power at least as the standard KL-based mutual information.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3507-3511
Number of pages5
ISBN (Electronic)9781479981311
DOIs
StatePublished - May 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Jensen-Shannon (JS) divergence
  • information bottleneck
  • pairwise clustering

Fingerprint

Dive into the research topics of 'Information-bottleneck Based on the Jensen-shannon Divergence with Applications to Pairwise Clustering'. Together they form a unique fingerprint.

Cite this