… approaches to matrix completion are sensitive to outliers and impulsive noise. This paper
develops robust and computationally efficient M-estimation based matrix completion algorithms…

… the robust matrix completion problem into a set of regression M-estimation problems, is …
version of the aforementioned robust factorized matrix completion approaches, for which we …

… Besides, the works in [27] and [28] suggest adopting the M-estimators to deal with robust
MC (RMC). Nevertheless, M-estimation has a breakdown point that depends on the largest …

… On iterative hard thresholding methods for high-dimensional m-estimation. In Advances
in Neural Information Processing Systems 27: Annual Conference on Neural Information …

… Cauchy have been widely adopted for robustness against outliers, but they also … robust loss
functions. Targeting on the application of robust matrix completion, efficient algorithms based …

… to use M-estimation in the problem of matrix factorization for low-rank matrix recovery. The
… Especially the Huber M-estimation enjoys good robustness property, and the loss function is …

… One message of the present work is that M-estimation with the Sorted Huber’s loss seem to
have theoretical improvements compared to Huber regression: it achieves the subgaussian …

… In this paper, based on the factorization framework, we propose a novel robust matrix
completion … In addition, robust M-estimation based matrix completion method [33] is developed, …

… for robust Kalman smoothing based on M-estimation. Specifically, we formulate the robust
… for measurement fitting in GKS by a robust cost from the M-estimation theory, which is then …

… We consider high dimensional M-estimation in settings where the response Y is … robust
(DDR) estimator of θ0 based on a high dimensional adaptation of the traditional double robust (…