Abstract:
Robust matrix completion allows for estimating a low-rank matrix based on a subset of its entries, even in presence of impulsive noise and outliers. We explore recent pro...Show MoreMetadata
Abstract:
Robust matrix completion allows for estimating a low-rank matrix based on a subset of its entries, even in presence of impulsive noise and outliers. We explore recent progress in the theoretical analysis of non-convex low-rank factorization problems to develop a robust approach that is based on a fast factorization method. We propose an algorithm that uses joint regression and scale estimation to compute the estimates. We prove that our algorithm converges to a global minimum with random initialization. An example function for which the guarantees hold is the pseudo-Huber function. In simulations, the proposed approach is compared to state-of the art robust and nonrobust methods. In addition, its applicability to image inpainting and occlusion removal is demonstrated.
Published in: 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)
Date of Conference: 21-24 September 2020
Date Added to IEEE Xplore: 20 October 2020
ISBN Information:
Print on Demand(PoD) ISSN: 1551-2541