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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. KI 2009: Advances in Artificial Intelligence, 144-152.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C309-E Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C30A-C
Genre: Conference Paper

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 Creators:
Jegelka, S1, Author           
Gretton, A1, Author           
Schölkopf, B1, Author           
Sriperumbudur, BK1, 2, Author           
von Luxburg, U1, Author           
Mertsching, Editor
B., Editor
Hund, M., Editor
Aziz, Z., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society, ou_1497647              

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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: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: URI: http://ki2009.uni-paderborn.de/
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)
Place of Event: Paderborn, Germany
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Title: KI 2009: Advances in Artificial Intelligence
Source Genre: Journal
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Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 144 - 152 Identifier: -