Sequence Segmentation via Clustering of Subsequences

Author

Garcia-Garcia, Daniel ; Parrado-Hernandez, Emilio ; Diaz-de-maria, Fernandeo

Author_Institution

Dept. of Signal Theor. & Commun., Univ. Carlos III de Madrid, Leganes, Spain

fYear

2009

fDate

13-15 Dec. 2009

Firstpage

229

Lastpage

234

Abstract

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.

Keywords

data mining; pattern clustering; probability; model-based distance measure; probabilistic generative model; semiparametric sequence clustering; sequence segmentation; spectral clustering; Approximation error; Character generation; Clustering algorithms; Data mining; Dynamic programming; Hidden Markov models; Labeling; Machine learning; Machine learning algorithms; Unsupervised learning; sequence segmentation; sequential data; spectral clustering;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Applications, 2009. ICMLA '09. International Conference on

Conference_Location

Miami Beach, FL

Print_ISBN

978-0-7695-3926-3

Type

conf

DOI

10.1109/ICMLA.2009.69

Filename

5381843