Split-Merge Incremental LEarning (SMILE) of Mixture Models

Abstract

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… (More)
DOI: 10.1007/978-3-540-74695-9_30

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Cite this paper

@inproceedings{Blekas2007SplitMergeIL, title={Split-Merge Incremental LEarning (SMILE) of Mixture Models}, author={Konstantinos Blekas and Isaac E. Lagaris}, booktitle={ICANN}, year={2007} }