Discovering hierarchical structure in normal relational data

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Hierarchical clustering is a widely used tool for structuring and visualizing complex data using similarity. Traditionally, hierarchical clustering is based on local heuristics that do not explicitly provide assessment of the statistical saliency of the extracted hierarchy. We propose a non-parametric generative model for hierarchical clustering of similarity based on multifurcating Gibbs fragmentation trees. This allows us to infer and display the posterior distribution of hierarchical structures that comply with the data. We demonstrate the utility of our method on synthetic data and data of functional brain connectivity.
Original languageEnglish
Title of host publicationProceedings of the 4th International Workshop on Cognitive Information Processing
Number of pages6
PublisherIEEE
Publication date2014
DOIs
Publication statusPublished - 2014
Event4th International Workshop on Cognitive Information Processing (CIP 2014) - Copenhagen, Denmark
Duration: 26 Apr 201428 Apr 2014
http://cip2014.conwiz.dk

Workshop

Workshop4th International Workshop on Cognitive Information Processing (CIP 2014)
CountryDenmark
CityCopenhagen
Period26/04/201428/04/2014
Internet address

Keywords

  • Bioengineering
  • Communication, Networking and Broadcast Technologies
  • Computing and Processing
  • Robotics and Control Systems
  • Signal Processing and Analysis

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