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Matrix completion via max-norm constrained optimization
Compressed sensing low-rank matrix matrix completion max-norm con-strained minimization optimal rate of convergence sparsity
2013/4/28
This paper studies matrix completion under a general sampling model using the max-norm as a convex relaxation for the rank of the matrix. The optimal rate of convergence is established for the Frobeni...
1-Bit Matrix Completion
1-Bit Matrix Completion
2012/11/22
In this paper we develop a theory of matrix completion for the extreme case of noisy 1-bit observations. Instead of observing a subset of the real-valued entries of a matrix M, we obtain a small numbe...
Weighted algorithms for compressed sensing and matrix completion
Compressed Sensing Weighted Basis-Pursuit Matrix Completion
2011/7/19
This paper is about iteratively reweighted basis-pursuit algorithms for compressed sensing and matrix completion problems. In a first part, we give a theoretical explanation of the fact that reweighte...
This paper considers the problem of matrix completion, when some number of the columns are arbitrarily corrupted, potentially by a malicious adversary. It is well-known that standard algorithms for ma...
We consider the problem of reconstructing a low
rank matrix from noisy observations of a subset of its entries.
This task has applications in statistical learning, computer vision,
and signal proce...
Let M be an nα × n matrix of rank r n, and assume that a uniformly random subset E of
its entries is observed. We describe an efficient algorithm that reconstructs M from |E| = O(r n)observed entries...