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• W2974257309 abstract "This paper presents a collaborative neurodynamic approach to sparse nonnegative matrix factorization (SNMF). SNMF is formulated as a bilevel optimization problem. In the lower level of the problem, the sparsity of factorized matrix is minimized subject to the factorization error and nonnegative constraints. In the upper level of the problem, the parameter of the inverted Gaussian function is minimized to approximate l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> norm. A group of neurodynamic models operating at two timescales is employed to solve the reformulated problem. The experimental results show the superiority of the proposed approach." @default.
• W2974257309 created "2019-09-26" @default.
• W2974257309 creator A5044406254 @default.
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• W2974257309 date "2019-08-01" @default.
• W2974257309 modified "2023-10-16" @default.
• W2974257309 title "Sparse Nonnegative Matrix Factorization Based on Collaborative Neurodynamic Optimization" @default.
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• W2974257309 doi "https://doi.org/10.1109/icist.2019.8836758" @default.
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