Work

On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion

Year: 2017

Type: article

Abstract: In this letter, we study the deterministic sampling patterns for the completion of low-rank matrix, when corrupted with a sparse noise, also known as robust matrix completion. We extend the recent results on the deterministic sampling patterns in the absence of noise based on the geometric analysis on the Grassmannian manifold. A special case where each column has a certain number of noisy entries is considered, where our probabilistic analysis performs very efficiently. Furthermore, assuming that the rank of the original matrix is not given, we provide an analysis to determine if the rank of a valid completion is indeed the actual rank of the data corrupted with sparse noise by verifying some conditions.

Source: IEEE Signal Processing Letters

DOI: 10.1109/lsp.2017.2780983

Authors: Morteza Ashraphijuo, Vaneet Aggarwal, Xiaodong Wang

Institutions: Columbia University, Purdue University West Lafayette

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Office of Naval Research

Awards: CIF: Medium Projects: Event-Triggered Sampling: Application to Decentralized Detection and Estimation, G7131722185