108 search results - page 1 / 22 » Clustering with Bregman Divergences |
ICASSP
2011
IEEE
13 years 1 months ago
2011
IEEE
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...
SDM
2004
SIAM
13 years 5 months ago
2004
SIAM
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...
Presentation
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
13 years 11 months ago
2009
Springer
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...
MA
2010
Springer
13 years 2 months ago
2010
Springer
This paper deals with the quantization problem of a random variable X taking values in a separable and re exive Banach space, and with the related question of clustering independ...