Information | |
Robust Sparse Representation for Incomplete and Noisy Data | |
Jiarong Shi1  Xiuyun Zheng2  Wei Yang2  | |
[1] School of Science, Xi’an University of Architecture and Technology, Xi’an 710055, China; | |
关键词:
sparse representation;
robust;
face classification;
alternating direction method of multipliers;
incomplete;
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DOI : 10.3390/info6030287 | |
来源: mdpi | |
【 摘 要 】
Owing to the robustness of large sparse corruptions and the discrimination of class labels, sparse signal representation has been one of the most advanced techniques in the fields of pattern classification, computer vision, machine learning and so on. This paper investigates the problem of robust face classification when a test sample has missing values. Firstly, we propose a classification method based on the incomplete sparse representation. This representation is boiled down to an
【 授权许可】
CC BY
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190011002ZK.pdf | 1032KB | download |