TY - GEN
T1 - Generalized clustering via kernel embeddings
AU - Jegelka, Stefanie
AU - Gretton, Arthur
AU - Schölkopf, Bernhard
AU - Sriperumbudur, Bharath K.
AU - Von Luxburg, Ulrike
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/76649122789
U2 - 10.1007/978-3-642-04617-9_19
DO - 10.1007/978-3-642-04617-9_19
M3 - Conference contribution
AN - SCOPUS:76649122789
SN - 3642046169
SN - 9783642046162
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 144
EP - 152
BT - KI 2009
T2 - 32nd Annual German Conference on Artificial Intelligence, KI 2009
Y2 - 15 September 2009 through 18 September 2009
ER -