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23

ICANN
2007
Springer

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Split-Merge Incremental LEarning (SMILE) of Mixture Models

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Split-Merge Incremental LEarning (SMILE) of Mixture Models

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In this article we present an incremental method for building a mixture model. Given the desired number of clusters K ≥ 2, we start with a two-component mixture and we optimize the likelihood by repeatedly applying a Split-Merge operation. When an optimum is obtained, we add a new component to the model by splitting in two, a properly chosen cluster. This goes on until the number of components reaches a preset limiting value. We have performed numerical experiments on several data–sets and report a performance comparison with other rival methods.

Konstantinos Blekas, Isaac E. Lagaris

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ICANN 2007 | Incremental Method | Mixture Model | Two-component Mixture |

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Added	08 Jun 2010
Updated	08 Jun 2010
Type	Conference
Year	2007
Where	ICANN
Authors	Konstantinos Blekas, Isaac E. Lagaris

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