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Title: Robust principal component analysis with complex noise
Authors: Zhao, Q
Mengt, D
Xut, Z
Zuo, W
Zhang, L 
Issue Date: 2014
Publisher: International Machine Learning Society (IMLS)
Source: 31st International Conference on Machine Learning, ICML 2014, 2014, v. 2, p. 1216-1226 How to cite?
Abstract: The research on robust principal component analysis (RPCA) has been attracting much atten-tion recently. The original RPCA model assumes sparse noise, and use the L1-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 Lp-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 uni-versal 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.
Description: 31st International Conference on Machine Learning, ICML 2014, Beijing, 21-26 June 2014
ISBN: 9781634393973
Appears in Collections:Conference Paper

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