Abstract
In this paper, the mixed-norm optimization is investigated for sparse signal reconstruction. Furthermore, an iterative optimization algorithm based on the projection method is presented for face recognition. From the theoretical point of view, the optimality and convergence of the proposed algorithm is strictly proved. And from the application point of view, the mixed norm combines the \(L_1\) and \(L_2\) norms to give a sparse and collaborative representation for pattern recognition, which has higher recognition rate than sparse representation algorithms. The algorithm is designed by combining the projection operator onto a box set with the projection matrix, which is effective to guarantee the feasibility of the optimal solution. Moreover, numerical experiments on randomly generated signals and three face image data sets are presented to show that the mixed-norm minimization is a combination of sparse representation and collaborative representation for pattern classification.
Q. Liu—This work was supported in part by the National Natural Science Foundation of China under Grant 61876036, the “333 Engineering” Foundation of Jiangsu Province of China under Grant BRA2018329, and the Fundamental Research Funds for the Central Universities.
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Liu, Q., Xiong, J., Yang, S. (2019). Mixed-Norm Projection-Based Iterative Algorithm for Face Recognition. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_33
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