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所属单位：理学院

发表刊物：SCIENCE CHINA-MATHEMATICS

关键字：pseudo matrix norm image set-based face recognition practical IQM

摘要：The Schatten p-quasi-norm regularized minimization problem has attracted extensive attention in machine learning, image recognition, signal reconstruction, etc. Meanwhile, the l (2,1)-regularized matrix optimization models are also popularly used for its joint sparsity. Naturally, the pseudo matrix norm l (2,p) is expected to carry over the advantages of both l (p) and l (2,1). This paper proposes a mixed l (2,q) -l (2,p) matrix minimization approach for multi-image face recognition. To uniformly solve this optimization problem for any q a [1, 2] and p a (0, 2], an iterative quadratic method (IQM) is developed. IQM is proved to descend strictly until it gets a stationary point of the mixed l (2,q)-l (2,p) matrix minimization. Moreover, a more practical IQM is presented for large-scale case. Experimental results on three public facial image databases show that the joint matrix minimization approach with practical IQM not only saves much computational cost but also achieves better performance in face recognition than state-of-the-art methods.

ISSN号：1674-7283

是否译文：否

发表时间：2018-07-01

合写作者：Luo, Aiwen

通讯作者：王丽平