Sorry, you need to enable JavaScript to visit this website.

ROBUST M-ESTIMATION BASED MATRIX COMPLETION

Citation Author(s):
Michael Muma, Wen-Jun Zeng, Abdelhak M. Zoubir
Submitted by:
Michael Muma
Last updated:
27 May 2019 - 11:28am
Document Type:
Poster
Document Year:
2019
Event:
Paper Code:
3482

Abstract 

Abstract: 

Conventional approaches to matrix completion are sensitive to outliers and impulsive noise. This paper develops robust and computationally efficient M-estimation based matrix completion algorithms. By appropriately arranging the observed entries, and then applying alternating minimization, the robust matrix completion problem is converted into a set of regression M-estimation problems. Making use of differ- entiable loss functions, the proposed algorithm overcomes a weakness of the lp-loss (p ≤ 1), which easily gets stuck in an inferior point. We prove that our algorithm converges to a stationary point of the nonconvex problem. Huber’s joint M-estimate of regression and scale can be used as a robust starting point for Tukey’s redescending M-estimator of re- gression based on an auxiliary scale. Numerical experiments on synthetic and real-world data demonstrate the superiority to state-of-the-art approaches.

up
0 users have voted:

Dataset Files

ICASSP_2019_Robust_M_Estimation_Based_Matrix_Completion_Poster.pdf

(259)