Generalized Clustering via Kernel Embeddings

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.

• Type: Conference
• Year: 2009
• Where: KI
• Authors: Stefanie Jegelka, Arthur Gretton, Bernhard Schölkopf, Bharath K. Sriperumbudur, Ulrike von Luxburg
• Keywords: Artificial Intelligence, Common Clustering Concepts, Corresponding Clustering Objective, KI 2009, Non-parametric Mixture Model