Clustering via Kernel Decomposition
IEEE Transactions on Neural NetworksPublished 1 January 2006Open access
A. Szymkowiak-Have, Mark Girolami, Jan Larsen
Citations27
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TL;DR
In this letter, the affinity matrix is created from the elements of a nonparametric density estimator and then decomposed to obtain posterior probabilities of class membership.
Abstract
Spectral clustering methods were proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this letter, the affinity matrix is created from the elements of a nonparametric density estimator and then decomposed to obtain posterior probabilities of class membership. Hyperparameters are selected using standard cross-validation methods.
Keywords
Computer Science
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