文章基本信息

标题：Robust Sparse Representation for Incomplete and Noisy Data
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作者：Jiarong Shi ; Xiuyun Zheng
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2015
卷号：6
期号：3
页码：287-299
DOI：10.3390/info6030287
语种：English
出版社：MDPI Publishing
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.
关键词：sparse representation; robust; face classification; alternating direction method of multipliers; incomplete; l ₁ minimization sparse representation ; robust ; face classification ; alternating direction method of multipliers ; incomplete ; l ₁ minimization