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» Structured Low Rank Approximation of a Bezout Matrix
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ICCV
2011
IEEE
12 years 8 months ago
Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction
Low-Rank Representation (LRR) [16, 17] is an effective method for exploring the multiple subspace structures of data. Usually, the observed data matrix itself is chosen as the dic...
Guangcan Liu, Shuicheng Yan
KDD
2012
ACM
201views Data Mining» more  KDD 2012»
11 years 11 months ago
Low rank modeling of signed networks
Trust networks, where people leave trust and distrust feedback, are becoming increasingly common. These networks may be regarded as signed graphs, where a positive edge weight cap...
Cho-Jui Hsieh, Kai-Yang Chiang, Inderjit S. Dhillo...
SIAMMAX
2010
97views more  SIAMMAX 2010»
13 years 3 months ago
Krylov Subspace Methods for Linear Systems with Tensor Product Structure
The numerical solution of linear systems with certain tensor product structures is considered. Such structures arise, for example, from the finite element discretization of a line...
Daniel Kressner, Christine Tobler
MOR
2008
110views more  MOR 2008»
13 years 8 months ago
On the Low Rank Solutions for Linear Matrix Inequalities
In this paper we present a polynomial-time procedure to find a low rank solution for a system of Linear Matrix Inequalities (LMI). The existence of such a low rank solution was sh...
Wenbao Ai, Yongwei Huang, Shuzhong Zhang
ICML
2003
IEEE
14 years 9 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