《山东大学学报(理学版)》 ›› 2023, Vol. 58 ›› Issue (3): 101-108.doi: 10.6040/j.issn.1671-9352.0.2022.059
韦芳,王长鹏*
WEI Fang, WANG Chang-peng*
摘要: 为了更好地拟合复杂噪声,增强低秩矩阵分解模型的鲁棒性,将双高斯先验引入到传统的高斯混合模型中,提出了基于双高斯先验的低秩矩阵分解(low-rank matrix factorization with double Gaussian prior, DGP-LRMF)模型,通过模型分解得到的2个矩阵均服从高斯先验,从而实现对噪声的有效建模,并在贝叶斯理论框架下利用EM算法实现模型参数的推断。实验结果验证了所提模型能够有效地处理含有复杂噪声的数据,取得了更优且更具稳定性的去噪效果。
中图分类号:
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