Clustering via Kernel Decomposition |
Anna Szymkowiak Have, Mark A. Girolami, Jan Larsen
|
| Abstract | Methods 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. |
| Keywords | probabilistic clustering, spectral clustering, kernel decomposition, aggregated Markov models, kernel principal component analysis |
| Type | Journal paper [With referee] |
| Journal | IEEE Transactions on Neural Networks |
| Year | 2006 Month January Vol. 17 No. 1 pp. 256-264 |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |