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 aweighted singular value thresholding (WSVT) problem as formulated 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.
|Original language||English (US)|
|Title of host publication||2018 5th International Conference on Systems and Informatics (ICSAI)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||5|
|State||Published - Jan 3 2019|