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ICASSP
2011
IEEE
8 years 6 months ago
Improved thresholds for rank minimization
—Nuclear norm minimization (NNM) has recently gained attention for its use in rank minimization problems. In this paper, we define weak, sectional and strong recovery for NNM to...
Samet Oymak, M. Amin Khajehnejad, Babak Hassibi
CORR
2011
Springer
157views Education» more  CORR 2011»
8 years 6 months ago
Large-Scale Convex Minimization with a Low-Rank Constraint
We address the problem of minimizing a convex function over the space of large matrices with low rank. While this optimization problem is hard in general, we propose an efficient...
Shai Shalev-Shwartz, Alon Gonen, Ohad Shamir
CORR
2011
Springer
202views Education» more  CORR 2011»
8 years 9 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...
JMLR
2010
128views more  JMLR 2010»
8 years 9 months ago
Fluid Dynamics Models for Low Rank Discriminant Analysis
We consider the problem of reducing the dimensionality of labeled data for classification. Unfortunately, the optimal approach of finding the low-dimensional projection with minim...
Yung-Kyun Noh, Byoung-Tak Zhang, Daniel D. Lee
CORR
2010
Springer
208views Education» more  CORR 2010»
8 years 11 months ago
Real-time Robust Principal Components' Pursuit
In the recent work of Candes et al, the problem of recovering low rank matrix corrupted by i.i.d. sparse outliers is studied and a very elegant solution, principal component pursui...
Chenlu Qiu, Namrata Vaswani
FFA
2007
59views more  FFA 2007»
9 years 2 months ago
Error-correcting codes on low rank surfaces
In this paper we construct some algebraic geometric error-correcting codes on surfaces whose Neron-Severi group has low rank. If the rank of the Neron-Severi group is 1, the inters...
Marcos Zarzar
SDM
2007
SIAM
143views Data Mining» more  SDM 2007»
9 years 4 months ago
Less is More: Compact Matrix Decomposition for Large Sparse Graphs
Given a large sparse graph, how can we find patterns and anomalies? Several important applications can be modeled as large sparse graphs, e.g., network traffic monitoring, resea...
Jimeng Sun, Yinglian Xie, Hui Zhang, Christos Falo...
IFIP
2005
Springer
9 years 8 months ago
A New Low Rank Quasi-Newton Update Scheme for Nonlinear Programming
A new quasi-Newton scheme for updating a low rank positive semi-definite Hessian approximation is described, primarily for use in sequential quadratic programming methods for non...
R. Fletcher
SCIA
2009
Springer
305views Image Analysis» more  SCIA 2009»
9 years 9 months ago
A Convex Approach to Low Rank Matrix Approximation with Missing Data
Many computer vision problems can be formulated as low rank bilinear minimization problems. One reason for the success of these problems is that they can be efficiently solved usin...
Carl Olsson, Magnus Oskarsson
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