Aybat, Necdet; Goldfarb, Donald; Ma, Shiqian - In: Computational Optimization and Applications 58 (2014) 1, pp. 1-29
The problem of recovering a low-rank matrix from a set of observations corrupted with gross sparse error is known as the robust principal component analysis (RPCA) and has many applications in computer vision, image processing and web data ranking. It has been shown that under certain...