Fast communication: Some empirical advances in matrix completion

Authors:
A. Majumdar;R. K. Ward
Affiliations:
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada V6T1Z4;Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada V6T1Z4
Venue:
Signal Processing
Year:
2011

Citing 6
Cited 0

Rank reduction and volume minimization approach to state-space subspace system identification

Signal Processing - Special section: Distributed source coding
Review: Adaptive total variation image deblurring: A majorization-minimization approach

Signal Processing
Exact Matrix Completion via Convex Optimization

Foundations of Computational Mathematics
SVD-based joint azimuth/elevation estimation with automatic pairing

Signal Processing
The power of convex relaxation: near-optimal matrix completion

IEEE Transactions on Information Theory
A Singular Value Thresholding Algorithm for Matrix Completion

SIAM Journal on Optimization

Quantified Score

Hi-index	0.08

Visualization

Abstract

Solving the matrix completion problem via rank minimization is NP hard. Recent studies have shown that this problem can be addressed as a convex nuclear-norm minimization problem, albeit at an increase in the required number of samples. This paper proposes a non-convex optimization problem (a variant of convex nuclear-norm minimization) for the solution of matrix completion. It also develops a fast numerical algorithm to solve the optimization. This empirical study shows that significant improvement can be achieved by the proposed method compared to the previous ones. The number of required samples is also dependent on the type of sampling scheme used. This work shows that blue-noise sampling schemes yield more accurate matrix completion results compared to the popular uniform random sampling.