Robust Matrix Completion via Joint Schatten p-Norm and Lp-Norm Minimization
Feiping Nie, Hua Wang, Heng Huang, Chris Ding
KAIS - 2013
Thelow-rankmatrixcompletionproblemisafundamentalmachinelearningand 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-norm and lp-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.
Links
- View publications from Hua Wang
- View publications researching Matrix/Tensor Completion
- View publications researching Robust Learning Models
Cite this paper
MLA
Nie, Feiping, et al. "Robust matrix completion via joint schatten p-norm and lp-norm minimization." 2013 Knowledge and Information Systems. KAIS, 2013.
BibTeX
@inproceedings{nie2013robust,
title={Robust matrix completion via joint schatten p-norm and lp-norm minimization},
author={Nie, Feiping and Wang, Hua and Cai, Xiao and Huang, Heng and Ding, Chris},
booktitle={Knowledge and Information Systems},
pages={525--544},
year={2013},
organization={KAIS}
}