Clusterpath: An algorithm for clustering using convex fusion penalties

Toby Dylan Hocking, Armand Joulin, Francis Bach, Jean Philippe Vert

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

137 Scopus citations

Abstract

We present a new clustering algorithm by proposing a convex relaxation of hierarchical clustering, which results in a family of objective functions with a natural geometric interpretation. We give efficient algorithms for calculating the continuous regularization path of solutions, and discuss relative advantages of the parameters. Our method experimentally gives state-of-the-art results similar to spectral clustering for non-convex clusters, and has the added benefit of learning a tree structure from the data.

Original languageEnglish (US)
Title of host publicationProceedings of the 28th International Conference on Machine Learning, ICML 2011
Pages745-752
Number of pages8
StatePublished - 2011
Externally publishedYes
Event28th International Conference on Machine Learning, ICML 2011 - Bellevue, WA, United States
Duration: Jun 28 2011Jul 2 2011

Publication series

NameProceedings of the 28th International Conference on Machine Learning, ICML 2011

Conference

Conference28th International Conference on Machine Learning, ICML 2011
Country/TerritoryUnited States
CityBellevue, WA
Period6/28/117/2/11

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction
  • Education

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