所属单位：

理学院

发表刊物：

Information

关键字：

中文关键字：稀疏表示，英文关键字：sparse representation; robust; face classification

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 l 1 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.

备注：

EI

第一作者：

杨威,郑秀云,史加荣

论文类型：

期刊论文

卷号：

卷：6

期号：

期：3

页面范围：

页：287-299

是否译文：

否

发表时间：

2015-06-01