Generalized clustering via kernel embeddings

Stefanie Jegelka, Arthur Gretton, Bernhard Schölkopf, Bharath K. Sriperumbudur, Ulrike Von Luxburg

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

19 Scopus citations

Abstract

We generalize traditional goals of clustering towards distinguishing components in a non-parametric mixture model. The clusters are not necessarily based on point locations, but on higher order criteria. This framework can be implemented by embedding probability distributions in a Hilbert space. The corresponding clustering objective is very general and relates to a range of common clustering concepts.

Original languageEnglish (US)
Title of host publicationKI 2009
Subtitle of host publicationAdvances in Artificial Intelligence - 32nd Annual German Conference on AI, Proceedings
Pages144-152
Number of pages9
DOIs
StatePublished - Dec 1 2009
Event32nd Annual German Conference on Artificial Intelligence, KI 2009 - Paderborn, Germany
Duration: Sep 15 2009Sep 18 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5803 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other32nd Annual German Conference on Artificial Intelligence, KI 2009
CountryGermany
CityPaderborn
Period9/15/099/18/09

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Generalized clustering via kernel embeddings'. Together they form a unique fingerprint.

Cite this