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Generalized Clustering via Kernel Embeddings

2009

Conference Paper

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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.

Author(s): Jegelka, S. and Gretton, A. and Schölkopf, B. and Sriperumbudur, BK. and von Luxburg, U.
Book Title: KI 2009: AI and Automation, Lecture Notes in Computer Science, Vol. 5803
Journal: KI 2009: Advances in Artificial Intelligence
Pages: 144-152
Year: 2009
Month: September
Day: 0
Editors: B Mertsching and M Hund and Z Aziz
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Address: Berlin, Germany
Digital: 0
DOI: 10.1007/978-3-642-04617-9_19
Event Name: 32nd Annual Conference on Artificial Intelligence (KI)
Event Place: Paderborn, Germany
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{5928,
  title = {Generalized Clustering via Kernel Embeddings},
  author = {Jegelka, S. and Gretton, A. and Sch{\"o}lkopf, B. and Sriperumbudur, BK. and von Luxburg, U.},
  journal = {KI 2009: Advances in Artificial Intelligence},
  booktitle = {KI 2009: AI and Automation, Lecture Notes in Computer Science, Vol. 5803},
  pages = {144-152},
  editors = {B Mertsching and M Hund and Z Aziz},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2009},
  month_numeric = {9}
}