Robust Probabilistic Matrix Tri-factorization

• Main Author: SHI Jiarong, CHEN Jiaojiao
• Format: Article
• Language: zho
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2020-07-01
• Series: Jisuanji kexue yu tansuo
• Subjects: matrix tri-factorization; probabilistic matrix factorization; expectation maximization; maximum a posterior estimation
• Online Access: http://fcst.ceaj.org/CN/abstract/abstract2274.shtml

Matrix factorization is a commonly-used data analysis tool in computer vision, machine learning and data mining. In recent years, the probabilistic models of matrix factorization have become the focus of attention. Existing probabilistic matrix factorization models generally decompose a given data matrix into the product of two low rank matrices, which probably limits the flexibility and practicability of these models. To address this issue, this paper proposes a model of robust probabilistic matrix tri-factorization (RPMTF). This model factorizes the data matrix into the product of three matrices and the robustness is taken into account simultaneously. A hierarchical representation of the Laplace distribution is firstly adopted for solving the proposed model. Then an expectation maximization algorithm is designed based on a strategy of maximum a posterior estimation. In the experiment, RPMTF is applied to image denoising and video background modeling, and the results verify the feasibility and effectiveness of the proposed method.