A Survey on Matrix Completion: Perspective of Signal Processing
arXiv:1901.10885
Abstract
Matrix completion (MC) is a promising technique which is able to recover an intact matrix with low-rank property from sub-sampled/incomplete data. Its application varies from computer vision, signal processing to wireless network, and thereby receives much attention in the past several years. There are plenty of works addressing the behaviors and applications of MC methodologies. This work provides a comprehensive review for MC approaches from the perspective of signal processing. In particular, the MC problem is first grouped into six optimization problems to help readers understand MC algorithms. Next, four representative types of optimization algorithms solving the MC problem are reviewed. Ultimately, three different application fields of MC are described and evaluated.
arXiv comment12 pages, 9 figures
References in corpus (5)
- The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices
- Optimum Design for Coexistence Between Matrix Completion Based MIMO Radars and a MIMO Communication System
- Online Robust Subspace Tracking from Partial Information
- Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning
- Target Estimation in Colocated MIMO Radar via Matrix Completion