Joint Schatten p-Norm and lp-Norm Robust Matrix Completion for Missing Value Recovery

View Researcher II's Other Codes

Disclaimer: “The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).”

Please contact us in case of a broken link from here

Authors Feiping Nie, Hua Wang, Heng Huang, Chris Ding
Journal/Conference Name Knowledge and Information Systems (KAIS)
Paper Category
Paper Abstract The low-rank matrix completion problem is a fundamental machine learning and data mining problem with many important applications. The standard low-rank matrix completion methods relax the rank minimization problem by the trace norm minimization. However, this relaxation may make the solution seriously deviate from the original solution. Meanwhile, most completion methods minimize the squared prediction errors on the observed entries, which is sensitive to outliers. In this paper, we propose a new robust matrix completion method to address these two problems. The joint Schatten $$p$$ p -norm and $$\ell _p$$ â„“ p -norm are used to better approximate the rank minimization problem and enhance the robustness to outliers. The extensive experiments are performed on both synthetic data and real-world applications in collaborative filtering prediction and social network link recovery. All empirical results show that our new method outperforms the standard matrix completion methods
Date of publication 2013
Code Programming Language MATLAB
Comment

Copyright Researcher II 2021