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Low-Rank Representation for Incomplete Data
Joint Authors
Yong, Longquan
Shi, Jiarong
Yang, Wei
Zheng, Xiuyun
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-12-30
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Low-rank matrix recovery (LRMR) has been becoming an increasingly popular technique for analyzing data with missing entries, gross corruptions, and outliers.
As a significant component of LRMR, the model of low-rank representation (LRR) seeks the lowest-rank representation among all samples and it is robust for recovering subspace structures.
This paper attempts to solve the problem of LRR with partially observed entries.
Firstly, we construct a nonconvex minimization by taking the low rankness, robustness, and incompletion into consideration.
Then we employ the technique of augmented Lagrange multipliers to solve the proposed program.
Finally, experimental results on synthetic and real-world datasets validate the feasibility and effectiveness of the proposed method.
American Psychological Association (APA)
Shi, Jiarong& Yang, Wei& Yong, Longquan& Zheng, Xiuyun. 2014. Low-Rank Representation for Incomplete Data. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1044270
Modern Language Association (MLA)
Shi, Jiarong…[et al.]. Low-Rank Representation for Incomplete Data. Mathematical Problems in Engineering No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1044270
American Medical Association (AMA)
Shi, Jiarong& Yang, Wei& Yong, Longquan& Zheng, Xiuyun. Low-Rank Representation for Incomplete Data. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1044270
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1044270