Feature and Nuclear Norm Minimization for Matrix Completion
发布时间：2025-03-05
点击次数：
DOI码：10.1109/TKDE.2020.3005978
发表刊物：IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
关键字：ALGORITHM，EQUATIONS
摘要：Matrix completion, whose goal is to recover a matrix from a few entries observed, is a fundamental model behind many applications. Our study shows that, in many applications, the to-be-complete matrix can be represented as the sum of a low-rank matrix and a sparse matrix associating with side information matrices. The low-rank matrix depicts the global patterns while the sparse matrix characterizes the local patterns, which are often described by the side information. Accordingly, to achieve high-quality matrix completion, we propose a Feature and Nuclear Norm Minimization (FNNM) model. The rationale of FNNM is to employ transductive completion to generalize the global pattern and inductive completion to recover the local pattern. Alternative minimization algorithm based on fixed-point iteration is developed to numerically solve the FNNM model. FNNM has demonstrated promising results on a variety of applications, including movie recommendation, drug-target interaction prediction, and multi-label learning, consistently outperforming the state-of-the-art matrix completion algorithms.
备注：Yang, MY (Yang, Mengyun) [1] , [2] ; Li, YH (Li, Yaohang) [3] ; Wang, JX (Wang, Jianxin) [1] , [4]
论文类型：期刊论文
卷号：34
期号：5
页面范围：2190-2199
是否译文：否
发表时间：2022-05-01
收录刊物：SCI