Robust Asymmetric Bayesian Adaptive Matrix Factorization

@inproceedings{Guo2017RobustAB,
  title={Robust Asymmetric Bayesian Adaptive Matrix Factorization},
  author={Xingjie Guo and Boyuan Pan and Deng Cai and Xiaofei He},
  booktitle={IJCAI},
  year={2017}
}
Low rank matrix factorizations(LRMF) have attracted much attention due to its wide range of applications in computer vision, such as image impainting and video denoising. Most of the existing methods assume that the loss between an observed measurement matrix and its bilinear factorization follows symmetric distribution, like gaussian or gamma families. However, in real-world situations, this assumption is often found too idealized, because pictures under various illumination and angles may… CONTINUE READING

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