PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A probabilistic approach to spectral clustering: Using KL divergence to find good clusters
Guido Sanguinetti, Jonathan Laidler and Neil Lawrence
In: Pascal Statistics and Optimization of Clustering Workshop 2005, 4-5 Jul 2005, London, UK.

Abstract

In previous work we have demonstrated a heuristic approach to spectral clustering which automatically determines the number of clusters that appear in the data set. Here we present a possible probabilistic interpretation for this, where the correct clustering can be found by minimising the Kullback-Leibler (KL) divergence between the affinity matrix and an appropriate Gaussian approximation.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1551
Deposited By:Jonathan Laidler
Deposited On:28 November 2005