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Conference Paper

Generalized Clustering via Kernel Embeddings

MPS-Authors
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Jegelka,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Gretton,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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von Luxburg,  U
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://link.springer.com/content/pdf/10.1007%2F978-3-642-04617-9_19.pdf
(Publisher version)

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Citation

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.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C309-E
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.