JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2023, Vol. 58 ›› Issue (3): 101-108.doi: 10.6040/j.issn.1671-9352.0.2022.059

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Low-rank matrix factorization with double Gaussian prior model

WEI Fang, WANG Chang-peng*   

  1. School of Science, Changan University, Xian 710064, Shaanxi, China
  • Published:2023-03-02

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Abstract: In order to fit the complex noise better and enhance the robustness of the low-rank matrix factorization model, the dual Gaussian priors are introduced into the traditional Gaussian mixture model, and the model of low-rank matrix factorization with double Gaussian prior(DGP-LRMF)is proposed, it is assumed that two matrices obtained by model decomposition obey Gaussian priori to realize the effective modeling for noise, and the EM algorithm is used to deduce the model parameters in the framework of Bayesian theory. Experimental results show that the proposed model can effectively deal with the data with complex noise and obtain better and more stable denoising performance.

Key words: Gaussian mixture model, low-rank matrix decomposition, Gaussian prior

CLC Number: 

  • TP391
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