Robust Principal Component Analysis with Complex Noise

被引:0
|
作者
Zhao, Qian [1 ]
Meng, Deyu [1 ]
Xu, Zongben [1 ]
Zuo, Wangmeng [2 ]
Zhang, Lei [3 ]
机构
[1] Xi An Jiao Tong Univ, Sch Math & Stat, Xian, Peoples R China
[2] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Peoples R China
[3] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China
来源
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 32 (CYCLE 2) | 2014年 / 32卷
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The research on robust principal component analysis (RPCA) has been attracting much attention recently. The original RPCA model assumes sparse noise, and use the L-1-norm to characterize the error term. In practice, however, the noise is much more complex and it is not appropriate to simply use a certain L-p-norm for noise modeling. We propose a generative RPCA model under the Bayesian framework by modeling data noise as a mixture of Gaussians (MoG). The MoG is a universal approximator to continuous distributions and thus our model is able to fit a wide range of noises such as Laplacian, Gaussian, sparse noises and any combinations of them. A variational Bayes algorithm is presented to infer the posterior of the proposed model. All involved parameters can be recursively updated in closed form. The advantage of our method is demonstrated by extensive experiments on synthetic data, face modeling and background subtraction.
引用
收藏
页码:55 / 63
页数:9
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