Bayesian k-Means as a "maximization-expectation" algorithm
We introduce a new class of "maximization-expectation" (ME) algorithms where we maximize over hidden variables but marginalize over random parameters. This reverses the roles of expectation and maximization in the classical expectation-maximization algorithm. In the context of clustering,...
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| Veröffentlicht in: | Neural computation Jg. 21; H. 4; S. 1145 |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
01.04.2009
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| Schlagworte: | |
| ISSN: | 0899-7667 |
| Online-Zugang: | Weitere Angaben |
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| Zusammenfassung: | We introduce a new class of "maximization-expectation" (ME) algorithms where we maximize over hidden variables but marginalize over random parameters. This reverses the roles of expectation and maximization in the classical expectation-maximization algorithm. In the context of clustering, we argue that these hard assignments open the door to very fast implementations based on data structures such as kd-trees and conga lines. The marginalization over parameters ensures that we retain the ability to infer model structure (i.e., number of clusters). As an important example, we discuss a top-down Bayesian k-means algorithm and a bottom-up agglomerative clustering algorithm. In experiments, we compare these algorithms against a number of alternative algorithms that have recently appeared in the literature. |
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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 0899-7667 |
| DOI: | 10.1162/neco.2008.12-06-421 |