Robust Matrix Completion via Joint Schatten p-Norm and Lp-Norm Minimization

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, Xiao Cai, Heng Huang, Chris Ding
Journal/Conference Name IEEE International Conference on Data Mining (ICDM)
Paper Category
Paper Abstract The low-rank matrix completion problem is a fundamental machine learning 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 $p$-norm and $\ell_p$-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 and social network link prediction. All empirical results show our new method outperforms the standard matrix completion methods.
Date of publication 2012
Code Programming Language MATLAB
Comment

Copyright Researcher II 2021