Adaptive quantile low-rank matrix factorization
作者：
Highlights：
• A new low-rank matrix factorization model is raised by modeling noise with a MoAL.
• The new method AQ-LRMF performs well for various kinds of noise.
• An EM-based efficient algorithm is provided to estimate the parameters in AQ_LRMF.
• Our model AQ-LRMF can automatically learn the weight of outliers.
• AQ-LRMF performs best in capturing local structural information in real images.
摘要
•A new low-rank matrix factorization model is raised by modeling noise with a MoAL.•The new method AQ-LRMF performs well for various kinds of noise.•An EM-based efficient algorithm is provided to estimate the parameters in AQ_LRMF.•Our model AQ-LRMF can automatically learn the weight of outliers.•AQ-LRMF performs best in capturing local structural information in real images.
论文关键词：Low-rank matrix factorization,Mixture of asymmetric Laplace distributions,Expectation maximization algorithm,Skew noise
论文评审过程：Received 24 May 2019, Revised 8 February 2020, Accepted 24 February 2020, Available online 25 February 2020, Version of Record 3 March 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107310
