A Fast Weighted Svt Algorithm

Singular value thresholding (SVT) plays an important role in the well-known robust principal component analysis (RPCA) algorithms which have many applications in machine learning, pattern recognition, and computer vision. There are many versions of generalized SVT proposed by researchers to achieve improvement in speed or performance. In this paper, we propose a fast algorithm to solve a weighted singular value thresholding (WSVT) problem as formulated in In which uses a combination of the nuclear norm and a weighted Frobenius norm and has shown to be comparable with RPCA method in some real world applications.