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NONNEGATIVE TENSOR DECOMPOSITION VIA COLLABORATIVE NEURODYNAMIC OPTIMIZATION
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DOI：10.1137/23M1627304.png)
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Abstract
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This paper introduces a novel collaborative neuro dynamic model for computing nonnegative canonical polyadic decomposition (CPD). The model relies on a system of recurrent neural networks to solve the underlying nonconvex optimization problem associated with nonnegative CPD. Additionally, a discrete-time version of the continuous neural network is developed. To enhance the chances of reaching a potential global minimum, the recurrent neural networks are allowed to communicate and exchange information through particle swarm optimization (PSO). Convergence and stability analyses of both the continuous and discrete neuro dynamic models are thoroughly examined. Experimental evaluations are conducted on random and real-world datasets to demonstrate the effectiveness of the proposed approach.
Keywords:
neurodynamic
canonical polyadic decomposition
particle swarm optimization
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A nonnegative matrix factorization algorithm based on a discrete-time projection neural network.png)
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