State-space dynamics distance for clustering sequential data

PATTERN RECOGNITION

卷:44

Article

Garcia-Garcia, Dario¹  Parrado-Hernandez, Emilio¹  Diaz-de-Maria, Fernando¹ 

[1] Univ Carlos III Madrid, Escuela Politecn Super, Leganes 28911, Spain

关键词: Sequential data;    Clustering;    Hidden Markov models;   

DOI  :  10.1016/j.patcog.2010.11.018

来源: Elsevier

【 摘 要 】

This paper proposes a novel similarity measure for clustering sequential data. We first construct a common state space by training a single probabilistic model with all the sequences in order to get a unified representation for the dataset. Then, distances are obtained attending to the transition matrices induced by each sequence in that state space. This approach solves some of the usual overfitting and scalability issues of the existing semi-parametric techniques that rely on training a model for each sequence. Empirical studies on both synthetic and real-world datasets illustrate the advantages of the proposed similarity measure for clustering sequences. (C) 2010 Elsevier Ltd. All rights reserved.

【 授权许可】

Free