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