Sciweavers

Share
ICASSP
2011
IEEE

Non-flat clustering whith alpha-divergences

10 years 9 months ago
Non-flat clustering whith alpha-divergences
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 gener- alizes 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 α-divergences. With the framework for represen- tational Bregman divergences, we show that an α-divergence based k-means algorithm can be designed. We present pre- liminary experiments for clustering and image segmentation applications. Since α-divergences are the natural divergences for constant curvature spaces, these experiments are expected to give information on the structure of the data.
Olivier Schwander, Frank Nielsen
Added 28 Feb 2011
Updated 28 Feb 2011
Type Conference
Year 2011
Where ICASSP
Authors Olivier Schwander, Frank Nielsen
Comments (0)
books