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Published in:
2007 | OriginalPaper | Chapter
Split–Merge Incremental LEarning (SMILE) of Mixture Models
Authors : Konstantinos Blekas, Isaac E. Lagaris
Published in: Artificial Neural Networks – ICANN 2007
Publisher: Springer Berlin Heidelberg
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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.