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SIAMIS
2010
190views more  SIAMIS 2010»
11 years 4 months ago
Analysis and Generalizations of the Linearized Bregman Method
This paper analyzes and improves the linearized Bregman method for solving the basis pursuit and related sparse optimization problems. The analysis shows that the linearized Bregma...
Wotao Yin
SDM
2004
SIAM
212views Data Mining» more  SDM 2004»
11 years 11 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...
COMPUTING
2007
122views more  COMPUTING 2007»
11 years 9 months ago
Error estimation for Bregman iterations and inverse scale space methods in image restoration
In this paper we consider error estimation for image restoration problems based on generalized Bregman distances. This error estimation technique has been used to derive convergen...
Martin Burger, E. Resmerita, Lin He
ISAAC
2009
Springer
175views Algorithms» more  ISAAC 2009»
12 years 4 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
JMLR
2010
115views more  JMLR 2010»
11 years 8 months ago
Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes
The problem of computing a maximum a posteriori (MAP) configuration is a central computational challenge associated with Markov random fields. There has been some focus on “tr...
Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwr...
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