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» Generalized low rank approximations of matrices
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CORR
2011
Springer
202views Education» more  CORR 2011»
13 years 2 months ago
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
We analyze a class of estimators based on a convex relaxation for solving highdimensional matrix decomposition problems. The observations are the noisy realizations of the sum of ...
Alekh Agarwal, Sahand Negahban, Martin J. Wainwrig...
SIGECOM
2003
ACM
135views ECommerce» more  SIGECOM 2003»
14 years 22 days ago
Playing large games using simple strategies
We prove the existence of -Nash equilibrium strategies with support logarithmic in the number of pure strategies. We also show that the payoffs to all players in any (exact) Nash...
Richard J. Lipton, Evangelos Markakis, Aranyak Meh...
SDM
2011
SIAM
414views Data Mining» more  SDM 2011»
12 years 10 months ago
Clustered low rank approximation of graphs in information science applications
In this paper we present a fast and accurate procedure called clustered low rank matrix approximation for massive graphs. The procedure involves a fast clustering of the graph and...
Berkant Savas, Inderjit S. Dhillon
CVPR
2007
IEEE
14 years 9 months ago
Modeling Appearances with Low-Rank SVM
Several authors have noticed that the common representation of images as vectors is sub-optimal. The process of vectorization eliminates spatial relations between some of the near...
Lior Wolf, Hueihan Jhuang, Tamir Hazan
ICML
2003
IEEE
14 years 8 months ago
Weighted Low-Rank Approximations
We study the common problem of approximating a target matrix with a matrix of lower rank. We provide a simple and efficient (EM) algorithm for solving weighted low-rank approximat...
Nathan Srebro, Tommi Jaakkola