Clustering via Kernel Decomposition

Anna Szymkowiak Have, Mark A. Girolami, Jan Larsen

AbstractMethods for spectral clustering have been proposed
recently which rely on the eigenvalue decomposition of an affinity
matrix. In this work it is proposed that the affinity matrix
is created based on the elements of a non-parametric density
estimator. This matrix is then decomposed to obtain posterior
probabilities of class membership using an appropriate form of
nonnegative matrix factorization. The troublesome selection of
hyperparameters such as kernel width and number of clusters
can be obtained using standard cross-validation methods as is
demonstrated on a number of diverse data sets.
Keywordsprobabilistic clustering, spectral clustering, kernel decomposition, aggregated Markov models, kernel principal component analysis
TypeJournal paper [With referee]
JournalIEEE Transactions on Neural Networks
Year2006    Month January    Vol. 17    No. 1    pp. 256-264
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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