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CVPR
2010
IEEE
13 years 5 months ago
Efficient computation of robust low-rank matrix approximations in the presence of missing data using the L1 norm
The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer vision applications. The workhorse of this class of problems has long been the ...
Anders Eriksson, Anton van den Hengel
STOC
2007
ACM
146views Algorithms» more  STOC 2007»
14 years 8 months ago
Playing games with approximation algorithms
In an online linear optimization problem, on each period t, an online algorithm chooses st S from a fixed (possibly infinite) set S of feasible decisions. Nature (who may be adve...
Sham M. Kakade, Adam Tauman Kalai, Katrina Ligett
SODA
2001
ACM
110views Algorithms» more  SODA 2001»
13 years 9 months ago
Improved approximation algorithms for rectangle tiling and packing
We provide improved approximation algorithms for several rectangle tiling and packing problems (RTILE, DRTILE and d-RPACK) studied in the literature. Our algorithms are highly eff...
Piotr Berman, Bhaskar DasGupta, S. Muthukrishnan, ...
SODA
2010
ACM
164views Algorithms» more  SODA 2010»
14 years 5 months ago
Differentially Private Approximation Algorithms
Consider the following problem: given a metric space, some of whose points are "clients," select a set of at most k facility locations to minimize the average distance f...
Anupam Gupta, Katrina Ligett, Frank McSherry, Aaro...
CORR
2010
Springer
171views Education» more  CORR 2010»
13 years 7 months ago
Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity
A general framework for solving image inverse problems is introduced in this paper. The approach is based on Gaussian mixture models, estimated via a computationally efficient MAP...
Guoshen Yu, Guillermo Sapiro, Stéphane Mall...