A Fast Weighted SVT Algorithm

Aritra Dutta, Xin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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 [1], 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 languageEnglish (US)
Title of host publication2018 5th International Conference on Systems and Informatics (ICSAI)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Print)9781728101200
StatePublished - Jan 3 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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