Title :
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
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;
Conference_Titel :
Machine Learning and Applications, 2009. ICMLA '09. International Conference on
Conference_Location :
Miami Beach, FL
Print_ISBN :
978-0-7695-3926-3
DOI :
10.1109/ICMLA.2009.69