Name
Abstract | ||
|---|---|---|
The mixture modeling framework is widely used in many applications. In this paper, we propose a component reductiontechnique, that collapses a mixture model into a mixture with fewer components. For fitting a mixture model to data, the EM (Expectation-Maximization) algorithm is usually used. Our algorithm is derived by extending mixture model learning using the EM-algorithm.In this extension, a difficulty arises from the fact that some crucial quantities cannot be evaluated analytically. We overcome this difficulty by introducing an effective approximation. The effectiveness of our algorithm is demonstrated by applying it to a simple synthetic component reduction task and a phoneme clustering problem. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-69162-4_34 | ICONIP |
Keywords | Field | DocType |
phoneme clustering problem,hierarchical mixture model construction,component reduction,simple synthetic component reduction,effective approximation,crucial quantity,fewer component,component reductiontechnique,mixture model,mixture modeling framework,em algorithm,expectation maximization algorithm | Pattern recognition,Mixture modeling,Computer science,Artificial intelligence,Cluster analysis,Machine learning,Mixture model | Conference |
Volume | ISSN | Citations |
4985 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kumiko Maebashi | 1 | 0 | 0.68 |
| Nobuo Suematsu | 2 | 54 | 8.99 |
| Akira Hayashi | 3 | 51 | 9.08 |