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Generalized Clustering via Kernel Embeddings
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.
@inproceedings{5928, title = {Generalized Clustering via Kernel Embeddings}, journal = {KI 2009: Advances in Artificial Intelligence}, booktitle = {KI 2009: AI and Automation, Lecture Notes in Computer Science, Vol. 5803}, 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.}, 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}, slug = {5928}, author = {Jegelka, S. and Gretton, A. and Sch{\"o}lkopf, B. and Sriperumbudur, BK. and von Luxburg, U.}, month_numeric = {9} }