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Presentation
896views
10 years 11 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...
ICDM
2010
IEEE
135views Data Mining» more  ICDM 2010»
10 years 11 months ago
Learning a Bi-Stochastic Data Similarity Matrix
An idealized clustering algorithm seeks to learn a cluster-adjacency matrix such that, if two data points belong to the same cluster, the corresponding entry would be 1; otherwise ...
Fei Wang, Ping Li, Arnd Christian König
CORR
2006
Springer
154views Education» more  CORR 2006»
11 years 1 months ago
Functional Bregman Divergence and Bayesian Estimation of Distributions
Abstract--A class of distortions termed functional Bregman divergences is defined, which includes squared error and relative entropy. A functional Bregman divergence acts on functi...
B. A. Frigyik, Santosh Srivastava, Maya R. Gupta
SDM
2004
SIAM
189views Data Mining» more  SDM 2004»
11 years 2 months ago
An Abstract Weighting Framework for Clustering Algorithms
act Weighting Framework for Clustering Algorithms Richard Nock Frank Nielsen Recent works in unsupervised learning have emphasized the need to understand a new trend in algorithmi...
Richard Nock, Frank Nielsen
KDD
2004
ACM
158views Data Mining» more  KDD 2004»
12 years 1 months ago
A generalized maximum entropy approach to bregman co-clustering and matrix approximation
Co-clustering is a powerful data mining technique with varied applications such as text clustering, microarray analysis and recommender systems. Recently, an informationtheoretic ...
Arindam Banerjee, Inderjit S. Dhillon, Joydeep Gho...
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