PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Sequence Segmentation via Clustering of Subsequences
Darío García-García, Emilio Parrado-Hernández and Fernando Díaz-de-María
In: International Conference on Machine Learning and Applications, 13-15 Dec 2009, Miami Beach.


We propose a new algorithm for sequence segmentation based on recent advances in semi-parametric sequence clustering. This approach implies the use of model-based distance measures between sequences, as well as a variant of spectral clustering specially tailored for segmentation. The method is highly flexible since it allows for the use of any probabilistic generative model for the individual segments. The performance of the proposed algorithm is demonstrated using both a synthetic dataset and a speaker segmentation task.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5501
Deposited By:Darío García-García
Deposited On:08 March 2010