A nonnegative matrix factorization algorithm based on a discrete-time projection neural network
This paper presents an algorithm for nonnegative matrix factorization based on a biconvex optimization formulation. First, a discrete-time projection neural network is introduced. An upper bound of its step size is derived to guarantee the stability of the neural network. Then, an algorithm is propo...
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| Published in | Neural networks Vol. 103; pp. 63 - 71 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.07.2018
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| Abstract | This paper presents an algorithm for nonnegative matrix factorization based on a biconvex optimization formulation. First, a discrete-time projection neural network is introduced. An upper bound of its step size is derived to guarantee the stability of the neural network. Then, an algorithm is proposed based on the discrete-time projection neural network and a backtracking step-size adaptation. The proposed algorithm is proven to be able to reduce the objective function value iteratively until attaining a partial optimum of the formulated biconvex optimization problem. Experimental results based on various data sets are presented to substantiate the efficacy of the algorithm. |
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| AbstractList | This paper presents an algorithm for nonnegative matrix factorization based on a biconvex optimization formulation. First, a discrete-time projection neural network is introduced. An upper bound of its step size is derived to guarantee the stability of the neural network. Then, an algorithm is proposed based on the discrete-time projection neural network and a backtracking step-size adaptation. The proposed algorithm is proven to be able to reduce the objective function value iteratively until attaining a partial optimum of the formulated biconvex optimization problem. Experimental results based on various data sets are presented to substantiate the efficacy of the algorithm. This paper presents an algorithm for nonnegative matrix factorization based on a biconvex optimization formulation. First, a discrete-time projection neural network is introduced. An upper bound of its step size is derived to guarantee the stability of the neural network. Then, an algorithm is proposed based on the discrete-time projection neural network and a backtracking step-size adaptation. The proposed algorithm is proven to be able to reduce the objective function value iteratively until attaining a partial optimum of the formulated biconvex optimization problem. Experimental results based on various data sets are presented to substantiate the efficacy of the algorithm.This paper presents an algorithm for nonnegative matrix factorization based on a biconvex optimization formulation. First, a discrete-time projection neural network is introduced. An upper bound of its step size is derived to guarantee the stability of the neural network. Then, an algorithm is proposed based on the discrete-time projection neural network and a backtracking step-size adaptation. The proposed algorithm is proven to be able to reduce the objective function value iteratively until attaining a partial optimum of the formulated biconvex optimization problem. Experimental results based on various data sets are presented to substantiate the efficacy of the algorithm. |
| Author | Wang, Jun Che, Hangjun |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29642020$$D View this record in MEDLINE/PubMed |
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| Keywords | Biconvex optimization Nonnegative matrix factorization Discrete-time projection neural network |
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| Snippet | This paper presents an algorithm for nonnegative matrix factorization based on a biconvex optimization formulation. First, a discrete-time projection neural... |
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| SubjectTerms | Algorithms Biconvex optimization Databases, Factual - utilization Discrete-time projection neural network Neural Networks (Computer) Nonnegative matrix factorization Pattern Recognition, Automated - methods Pattern Recognition, Automated - trends Photic Stimulation - methods Time Factors |
| Title | A nonnegative matrix factorization algorithm based on a discrete-time projection neural network |
| URI | https://dx.doi.org/10.1016/j.neunet.2018.03.003 https://www.ncbi.nlm.nih.gov/pubmed/29642020 https://www.proquest.com/docview/2024470515 |
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