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MSDA-NMF: A Multilayer Complex System Model Integrating Deep Autoencoder and NMF

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  • Xiaoming Li

    (College of International Business, Zhejiang Yuexiu University, Shaoxing 312000, China
    Shaoxing Key Laboratory of Intelligent Monitoring and Prevention of Smart Society, Shaoxing 312000, China)

  • Wei Yu

    (College of International Business, Zhejiang Yuexiu University, Shaoxing 312000, China
    Shaoxing Key Laboratory of Intelligent Monitoring and Prevention of Smart Society, Shaoxing 312000, China)

  • Guangquan Xu

    (Tianjin Key Laboratory of Advanced Networking (TANK), College of Intelligence and Computing, Tianjin University, Tianjin 300350, China)

  • Fangyuan Liu

    (Graduate Business School, UCSI University, Kuala Lumpur 56000, Malaysia)

Abstract

In essence, the network is a way of encoding the information of the underlying social management system. Ubiquitous social management systems rarely exist alone and have dynamic complexity. For complex social management systems, it is difficult to extract and represent multi-angle features of data only by using non-negative matrix factorization. Existing deep NMF models integrating multi-layer information struggle to explain the results obtained after mid-layer NMF. In this paper, NMF is introduced into the multi-layer NMF structure, and the feature representation of the input data is realized by using the complex hierarchical structure. By adding regularization constraints for each layer, the essential features of the data are obtained by characterizing the feature transformation layer-by-layer. Furthermore, the deep autoencoder and NMF are fused to construct the multi-layer NMF model MSDA-NMF that integrates the deep autoencoder. Through multiple data sets such as HEP-TH, OAG and HEP-TH, Pol blog, Orkut and Livejournal, compared with 8 popular NMF models, the Micro index of the better model increased by 1.83 , NMI value increased by 12 % , and link prediction performance improved by 13 % . Furthermore, the robustness of the proposed model is verified.

Suggested Citation

  • Xiaoming Li & Wei Yu & Guangquan Xu & Fangyuan Liu, 2022. "MSDA-NMF: A Multilayer Complex System Model Integrating Deep Autoencoder and NMF," Mathematics, MDPI, vol. 10(15), pages 1-18, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2750-:d:879295
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    References listed on IDEAS

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    1. José M. Maisog & Andrew T. DeMarco & Karthik Devarajan & Stanley Young & Paul Fogel & George Luta, 2021. "Assessing Methods for Evaluating the Number of Components in Non-Negative Matrix Factorization," Mathematics, MDPI, vol. 9(22), pages 1-13, November.
    2. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
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