Affiliation of Author(s):理学院
Journal:SCIENCE CHINA-MATHEMATICS
Key Words:pseudo matrix norm image set-based face recognition practical IQM
Abstract: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 No.:1674-7283
Translation or Not:no
Date of Publication:2018-07-01
Co-author:Luo, Aiwen
Correspondence Author:WLP
Date of Publication:2018-07-01