Boolean matrix factorization based on collaborative neurodynamic optimization with Boltzmann machines
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Publication:6077040
DOI10.1016/j.neunet.2022.06.006WikidataQ114145544 ScholiaQ114145544MaRDI QIDQ6077040
Jun Wang, Sam Kwong, Xin-Qi Li
Publication date: 17 October 2023
Published in: Neural Networks (Search for Journal in Brave)
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Cites Work
- Unnamed Item
- Optimization by Simulated Annealing
- Boolean calculus of differences
- A nonnegative matrix factorization algorithm based on a discrete-time projection neural network
- A collaborative neurodynamic approach to global and combinatorial optimization
- Toward quality assessment of Boolean matrix factorizations
- A collective neurodynamic optimization approach to bound-constrained nonconvex optimization
- Boltzmann machines as a model for parallel annealing
- Nonnegative Matrix Factorization: Models, Algorithms and Applications
- Fast Nonnegative Matrix/Tensor Factorization Based on Low-Rank Approximation
- A Discrete-Time Neurodynamic Approach to Sparsity-Constrained Nonnegative Matrix Factorization
- Neural networks and physical systems with emergent collective computational abilities.
- Learning the parts of objects by non-negative matrix factorization
- Neurons with graded response have collective computational properties like those of two-state neurons.
- Cardinality-constrained portfolio selection based on collaborative neurodynamic optimization
- A one-layer recurrent neural network for nonsmooth pseudoconvex optimization with quasiconvex inequality and affine equality constraints
- A neurodynamic optimization approach to supervised feature selection via fractional programming