PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Clustering via Kernel Decomposition
Anna Szymkowiak-Have, Mark Girolami and Jan Larsen
IEEE Transactions on Neural Networks Volume 17, Number 1, pp. 256-264, 2006.

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
Information Retrieval & Textual Information Access
ID Code:2894
Deposited By:Jan Larsen
Deposited On:23 November 2006