PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A New Distance Measure for Model-Based Sequence Clustering
Dario Garcia-Garcia, Emilio Parrado-Hernandez and Fernando Diaz-de-MAria
IEEE Transactions on Pattern Analysis and Machine Intelligence 2008.

Abstract

We review the existing alternatives for defining model-based distances for clustering sequences and propose a new one based on the Kullback-Leibler divergence. This distance is shown to be especially useful in combination with spectral clustering. For improved performance in real-world scenarios, a model selection scheme is also proposed.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Speech
Theory & Algorithms
ID Code:4474
Deposited By:Emilio Parrado-Hernandez
Deposited On:13 March 2009