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Information 2015, 6(3), 287-299; doi:10.3390/info6030287

Robust Sparse Representation for Incomplete and Noisy Data

School of Science, Xi'an University of Architecture and Technology, Xi'an 710055, China
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Academic Editor: Willy Susilo
Received: 12 June 2015 / Revised: 15 June 2015 / Accepted: 16 June 2015 / Published: 24 June 2015
(This article belongs to the Section Information Applications)
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Abstract

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 l1 minimization problem and an alternating direction method of multipliers is employed to solve it. Then, we provide a convergent analysis and a model extension on incomplete sparse representation. Finally, we conduct experiments on two real-world face datasets and compare the proposed method with the nearest neighbor classifier and the sparse representation-based classification. The experimental results demonstrate that the proposed method has the superiority in classification accuracy, completion of the missing entries and recovery of noise. View Full-Text
Keywords: sparse representation; robust; face classification; alternating direction method of multipliers; incomplete; l1 minimization sparse representation; robust; face classification; alternating direction method of multipliers; incomplete; l1 minimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Shi, J.; Zheng, X.; Yang, W. Robust Sparse Representation for Incomplete and Noisy Data. Information 2015, 6, 287-299.

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