KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Visual Computing Center (VCC)
Online Publication Date2019-01-03
Print Publication Date2018-11
Permanent link to this recordhttp://hdl.handle.net/10754/630940
MetadataShow full item record
AbstractSingular 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.
CitationDutta A, Li X (2018) A Fast Weighted SVT Algorithm. 2018 5th International Conference on Systems and Informatics (ICSAI). Available: http://dx.doi.org/10.1109/icsai.2018.8599289.