Robust Sparse Representation for Incomplete and Noisy Data

Information

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;    l₁ minimization;   

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 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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.