Generalized clustering via kernel embeddings

Authors:
Stefanie Jegelka;Arthur Gretton;Bernhard Schölkopf;Bharath K. Sriperumbudur;Ulrike Von Luxburg
Affiliations:
Max Planck Institute for Biological Cybernetics, Tübingen, Germany;Max Planck Institute for Biological Cybernetics, Tübingen, Germany and Carnegie Mellon University, Pittsburgh, PA;Max Planck Institute for Biological Cybernetics, Tübingen, Germany;Dept. of ECE, UC San Diego, La Jolla, CA;Max Planck Institute for Biological Cybernetics, Tübingen, Germany
Venue:
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Year:
2009

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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.