Generalized clustering via kernel embeddings

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

doi:10.1007/978-3-642-04617-9_19

Generalized clustering via kernel embeddings

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

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Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

26 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 language	English (US)
Title of host publication	KI 2009
Subtitle of host publication	Advances in Artificial Intelligence - 32nd Annual German Conference on AI, Proceedings
Pages	144-152
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-642-04617-9_19
State	Published - 2009
Event	32nd Annual German Conference on Artificial Intelligence, KI 2009 - Paderborn, Germany Duration: Sep 15 2009 → Sep 18 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5803 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	32nd Annual German Conference on Artificial Intelligence, KI 2009
Country/Territory	Germany
City	Paderborn
Period	9/15/09 → 9/18/09

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

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10.1007/978-3-642-04617-9_19

Cite this

Jegelka, S., Gretton, A., Schölkopf, B., Sriperumbudur, B. K., & Von Luxburg, U. (2009). Generalized clustering via kernel embeddings. In KI 2009: Advances in Artificial Intelligence - 32nd Annual German Conference on AI, Proceedings (pp. 144-152). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5803 LNAI). https://doi.org/10.1007/978-3-642-04617-9_19

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title = "Generalized clustering via kernel embeddings",

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.",

author = "Stefanie Jegelka and Arthur Gretton and Bernhard Sch{\"o}lkopf and Sriperumbudur, {Bharath K.} and {Von Luxburg}, Ulrike",

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Jegelka, S, Gretton, A, Schölkopf, B, Sriperumbudur, BK & Von Luxburg, U 2009, Generalized clustering via kernel embeddings. in KI 2009: Advances in Artificial Intelligence - 32nd Annual German Conference on AI, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5803 LNAI, pp. 144-152, 32nd Annual German Conference on Artificial Intelligence, KI 2009, Paderborn, Germany, 9/15/09. https://doi.org/10.1007/978-3-642-04617-9_19

Generalized clustering via kernel embeddings. / Jegelka, Stefanie; Gretton, Arthur; Schölkopf, Bernhard et al.
KI 2009: Advances in Artificial Intelligence - 32nd Annual German Conference on AI, Proceedings. 2009. p. 144-152 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5803 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Generalized clustering via kernel embeddings

AU - Jegelka, Stefanie

AU - Gretton, Arthur

AU - Schölkopf, Bernhard

AU - Sriperumbudur, Bharath K.

AU - Von Luxburg, Ulrike

PY - 2009

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AB - 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.

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SN - 9783642046162

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - KI 2009

T2 - 32nd Annual German Conference on Artificial Intelligence, KI 2009

Y2 - 15 September 2009 through 18 September 2009

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Jegelka S, Gretton A, Schölkopf B, Sriperumbudur BK, Von Luxburg U. Generalized clustering via kernel embeddings. In KI 2009: Advances in Artificial Intelligence - 32nd Annual German Conference on AI, Proceedings. 2009. p. 144-152. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-04617-9_19

Generalized clustering via kernel embeddings

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