Abstract
In this paper, a collective neurodynamic optimization approach is proposed to nonnegative tensor factorization. Tensor decompositions are often applied in the data analysis. However, it is often a nonconvex optimization problem, which would cost much time and usually trap into the local minima. To solve this problem, a novel collective neurodynamic optimization approach is proposed by combining recurrent neural networks (RNN) and particle swarm optimization (PSO) algorithm. Each RNN still carries out local search. And then the best solution of each RNN improves through PSO framework. In the end, the global optimal solutions of nonnegative tensor factorization are obtained. Experiments results demonstrate the effectiveness for the nonconvex optimization with constraints.
J. Fan—The work described in the paper was supported by the National Key Research and Development Program of China under project 2016YFC1401007, the Foundation of High Resolution Special Research under 41-Y30B12-9001-14/16, and the National Natural Science Foundation of China under project 61273307.
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Fan, J., Wang, J. (2017). A Collective Neurodynamic Optimization Approach to Nonnegative Tensor Decomposition. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_25
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DOI: https://doi.org/10.1007/978-3-319-59081-3_25
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