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Generalized clustering via kernel embeddings

Jegelka, S; Gretton, A; Schölkopf, B; Sriperumbudur, BK; Von Luxburg, U; (2009) Generalized clustering via kernel embeddings. In: (pp. pp. 144-152).

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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. © 2009 Springer Berlin Heidelberg.

Type: Proceedings paper
Title: Generalized clustering via kernel embeddings
ISBN: 3642046169
DOI: 10.1007/978-3-642-04617-9_19
URI: http://discovery.ucl.ac.uk/id/eprint/1334305
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