PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Clustering via Kernel Decomposition
A Szymkowiak-Have, Mark Girolami and Jan Larsen
IEEE Transactions on Neural Networks 2005.

Abstract

Spectral clustering methods were proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this work the affinity matrix is created from the elements of a non-parametric density estimator and then decomposed to obtain posterior probabilities of class membership. Hyperparameters are selected using standard cross-validation methods.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:1606
Deposited By:Mark Girolami
Deposited On:28 November 2005