Weighted kernel deterministic annealing: A maximum-entropy principle approach for shape clustering

Mayank Baranwal, Srinivasa M. Salapaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Kernel k-means and spectral clustering methods have both been used extensively to cluster data that are non-linearly separable in input space. While there has been significant research since their inceptions, both the methods have some drawbacks. Similar to the basic k-means algorithm, the Kernel k-means algorithm is sensitive to initialization. On the other hand, the spectral methods are based on finding eigenvectors and can be computationally prohibitive. In this paper, we propose a novel maximum-entropy principle (MEP) based weighted-kernel deterministic annealing (WKDA) algorithm, which is independent of initialization and has ability to avoid poor local minima. Additionally, we show that the WKDA approach reduces to Kernel k-means approach as a special case. Finally, we extend the proposed algorithm to include constrained-clustering and present the results for a variety of interesting data sets.

Original languageEnglish (US)
Title of host publication2018 Indian Control Conference, ICC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538629048
DOIs
StatePublished - Mar 7 2018
Event4th Indian Control Conference, ICC 2018 - Kanpur, India
Duration: Jan 4 2018Jan 6 2018

Publication series

Name2018 Indian Control Conference, ICC 2018 - Proceedings
Volume2018-January

Other

Other4th Indian Control Conference, ICC 2018
CountryIndia
CityKanpur
Period1/4/181/6/18

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Optimization
  • Logic

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