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.
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Jegelka, S., Gretton, A., Schölkopf, B., Sriperumbudur, B.K., von Luxburg, U. (2009). Generalized Clustering via Kernel Embeddings. In: Mertsching, B., Hund, M., Aziz, Z. (eds) KI 2009: Advances in Artificial Intelligence. KI 2009. Lecture Notes in Computer Science(), vol 5803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04617-9_19
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DOI: https://doi.org/10.1007/978-3-642-04617-9_19
Publisher Name: Springer, Berlin, Heidelberg
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