ABSTRACT

Recovering low-rank and sparse matrices from incomplete or even corrupted observations is a common problem in many application areas, including statistics [1, 9, 51], bioinformatics [37], machine learning [28, 47, 49, 52], computer vision [5, 7, 42, 43, 58], and signal and image processing [27, 30, 38]. In these areas, data often have high dimensionality, such as digital photographs and surveillance videos, which makes inference, learning, and recognition infeasible due to the “curse of dimensionality.”