.png){kind=icon}
返回
Flexible robust principal component analysis
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-019-00999-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The error correction problem is a very important topic in machine learning. However, existing methods only focus on data recovery and ignore data compact representation. In this paper, we propose a flexible robust principal component analysis (FRPCA) method in which two different matrices are used to perform error correction and the data compact representation can be obtained by using one of matrices. Moreover, FRPCA selects the most relevant features to guarantee that the recovered data can faithfully preserve the original data semantics. The learning is done by solving a nuclear-norm regularized minimization problem, which is convex and can be solved in polynomial time. Experiments were conducted on image sequences containing targets of interest in a variety of environments, e.g., offices, campuses. We also compare our method with existing method in recovering the face images from corruptions. Experimental results show that the proposed method achieves better performances and it is more practical than the existing approaches.
Keyword:
Error correction
Robust principal component analysis (RPCA)
Subspace learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.7
论文数:
3.2K
被引数:
5.6K
机构
引用论文
Expression in Brain of a Messenger RNA Encoding a Novel Neuropeptide Homologous to Calcitonin Gene-Related Peptide.png)
Science
ScienceIF0
PREAI
分享收藏
查看更多内容