# Non-flat clustering whith alpha-divergences

Abstract : The scope of the well-known $k$-means algorithm has been broadly extended with some recent results: first, the k-means++ initialization method gives some approximation guarantees; second, the Bregman k-means algorithm generalizes the classical algorithm to the large family of Bregman divergences. The Bregman seeding framework combines approximation guarantees with Bregman divergences. We present here an extension of the k-means algorithm using the family of alpha-divergences. With the framework for representational Bregman divergences, we show that an alpha-divergence based k-means algorithm can be designed. We present preliminary experiments for clustering and image segmentation applications. Since alpha-divergences are the natural divergences for constant curvature spaces, these experiments are expected to give information on the structure of the data.
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Cited literature [10 references]

https://hal-polytechnique.archives-ouvertes.fr/hal-00708634
Contributor : Olivier Schwander <>
Submitted on : Friday, June 15, 2012 - 2:28:17 PM
Last modification on : Thursday, March 5, 2020 - 6:20:15 PM
Long-term archiving on: : Thursday, December 15, 2016 - 3:37:58 PM

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### Citation

Olivier Schwander, Frank Nielsen. Non-flat clustering whith alpha-divergences. ICASSP, May 2011, Prague, Czech Republic. pp.2100 - 2103, ⟨10.1109/ICASSP.2011.5946740⟩. ⟨hal-00708634⟩

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