Generalized Clustering via Kernel Embeddings
2009
Conference Paper
ei
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) |
DOI: | 10.1007/978-3-642-04617-9_19 |
Event Name: | 32nd Annual Conference on Artificial Intelligence (KI) |
Event Place: | Paderborn, Germany |
Address: | Berlin, Germany |
Digital: | 0 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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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}, doi = {10.1007/978-3-642-04617-9_19}, month_numeric = {9} } |