Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICMLA.2009.69 | ICMLA |
Keywords | Field | DocType |
probabilistic generative model,speaker segmentation task,sequence segmentation,recent advance,model-based distance measure,individual segment,synthetic dataset,new algorithm,proposed algorithm,semi-parametric sequence clustering,dynamic programming,probability,probabilistic logic,clustering algorithms,hidden markov models,data mining,spectral clustering,data models | Fuzzy clustering,Data mining,Scale-space segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Cluster analysis,Sequence clustering,Canopy clustering algorithm,Pattern recognition,Correlation clustering,Machine learning | Conference |
ISBN | Citations | PageRank |
978-0-7695-3926-3 | 1 | 0.36 |
References | Authors | |
11 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Darío García-García | 1 | 24 | 4.41 |
| Emilio Parrado-Hernández | 2 | 130 | 11.16 |
| Fernando Diaz-de-Maria | 3 | 9 | 1.21 |
| Parrado-Hernandez, E. | 4 | 1 | 0.36 |
| Diaz-de-Maria, F. | 5 | 1 | 0.36 |