LAUSR

The Schatten p-quasi-norm regularized minimization problem has attracted extensive attention in machine learning, image recognition, signal reconstruction, etc. Meanwhile, the l2,1-regularized matrix optimization models are also popularly used for its joint sparsity. Naturally, the pseudo matrix norm l2,p is expected to carry over the advantages of both lp and l2,1. This paper proposes a mixed l2,q-l2,p matrix minimization approach for multi-image face recognition. To uniformly solve this optimization problem for any q ∈ [1, 2] and p ∈ (0, 2], an iterative quadratic method (IQM) is developed. IQM is proved to descend strictly until it gets a stationary point of the mixed l2,q-l2,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.