Abstract
Matrix completion plays an important role in machine learning and data mining. Although a great number of algorithms have been developed for this issue, most of them can cope with only the Gaussian noise or sparse outliers. This paper focus on an intractable setting that the known entries are corrupted by Gaussian noise and sparse outliers simultaneously. Specifically, we construct a novel model with a loss function derived from the celebrated Huber function. Furthermore, an efficient optimization method is presented to solve the constructed model. The promising performance of our algorithm is demonstrated via numerous experiments on several benchmark datasets.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
