Journal article
Clustering via Kernel Decomposition
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.
| Language: | English |
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| Publisher: | IEEE |
| Year: | 2006 |
| Pages: | 256-264 |
| ISSN: | 10459227 and 19410093 |
| Types: | Journal article |
| DOI: | 10.1109/TNN.2005.860840 |
| ORCIDs: | Larsen, Jan |
aggregated Markov models kernel decomposition kernel principal component analysis probabilistic clustering spectral clustering
Aggregated Markov model Clustering methods Data mining Eigenvalues and eigenfunctions Feature extraction Informatics Kernel Markov processes Mathematical model Matrix decomposition Parameter estimation Principal component analysis affinity matrix eigenvalue decomposition eigenvalues and eigenfunctions kernel principal component analysis (KPCA) nonparametric density estimator posterior probabilities principal component analysis spectral clustering methods standard cross-validation methods
