Regularized Adaptive Huber Matrix Regression and Distributed Learning

Matrix regression provides a powerful technique for analyzing matrixtype data, as exemplified by many contemporary applications. Despite the rapid advance, distributed learning for robust matrix regression to deal with heavy-tailed noises in the big data regime still remains untouched. In this paper, we first consider adaptive Huber matrix regression with a nuclear norm penalty, which enjoys insensitivity to heavy-tailed noises without losing the statistical accuracy. To further enhance the scalability in massive data applications, we employ the communication-efficient surrogate likelihood framework to develop distributed robust matrix regression, which can be efficiently implemented through the ADMM algorithms. Under only bounded (1+ <i>δ</i>)-th moment on the noise for some <i>δ </i>ε (0, 1], we provide upper bounds for the estimation error of the central estimator and the distributed estimator, and prove they can achieve the same rate as established with sub-Gaussian tails when only the second moment of noise exists. Numerical studies verify the advantage of the proposed method over existing methods in heavy-tailed noise settings.