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  Generalized Clustering via Kernel Embeddings

Jegelka, S., Gretton, A., Schölkopf, B., Sriperumbudur, B., & von Luxburg, U. (2009). Generalized Clustering via Kernel Embeddings. In B. Mertsching, M. Hund, & Z. Aziz (Eds.), KI 2009: Advances in Artificial Intelligence: 32nd Annual German Conference on AI, Paderborn, Germany, September 15-18, 2009 (pp. 144-152). Berlin, Germany: Springer.

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 Creators:
Jegelka, S1, 2, Author           
Gretton, A1, 2, Author           
Schölkopf, B1, 2, Author           
Sriperumbudur, BK, Author           
von Luxburg, U1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2009-09
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-642-04617-9_19
BibTex Citekey: 5928
 Degree: -

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Title: 32nd Annual Conference on Artificial Intelligence (KI 2009)
Place of Event: Paderborn, Germany
Start-/End Date: 2009-09-15 - 2009-09-18

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Title: KI 2009: Advances in Artificial Intelligence: 32nd Annual German Conference on AI, Paderborn, Germany, September 15-18, 2009
Source Genre: Proceedings
 Creator(s):
Mertsching, B, Editor
Hund, M, Editor
Aziz, Z, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 144 - 152 Identifier: ISBN: 978-3-642-04616-2

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 5803 Sequence Number: - Start / End Page: - Identifier: -