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» Clustering with Bregman Divergences
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ICASSP
2011
IEEE
11 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...
Olivier Schwander, Frank Nielsen
SDM
2004
SIAM
212views Data Mining» more  SDM 2004»
12 years 1 months ago
Clustering with Bregman Divergences
A wide variety of distortion functions, such as squared Euclidean distance, Mahalanobis distance, Itakura-Saito distance and relative entropy, have been used for clustering. In th...
Arindam Banerjee, Srujana Merugu, Inderjit S. Dhil...

Presentation
896views
11 years 9 months ago
Exponential families and simplification of mixture models
Presentation of the exponential families, of the mixtures of such distributions and how to learn it. We then present algorithms to simplify mixture model, using Kullback-Leibler di...
ISAAC
2009
Springer
175views Algorithms» more  ISAAC 2009»
12 years 6 months ago
Worst-Case and Smoothed Analysis of k-Means Clustering with Bregman Divergences
The k-means algorithm is the method of choice for clustering large-scale data sets and it performs exceedingly well in practice. Most of the theoretical work is restricted to the c...
Bodo Manthey, Heiko Röglin
MA
2010
Springer
206views Communications» more  MA 2010»
11 years 10 months ago
Quantization and clustering with Bregman divergences
 This paper deals with the quantization problem of a random variable X taking values in a separable and reexive Banach space, and with the related question of clustering independ...
Aurélie Fischer
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