Adaptive Quantile Low-Rank Matrix Factorization

@article{Xu2020AdaptiveQL,
  title={Adaptive Quantile Low-Rank Matrix Factorization},
  author={Shuang Xu and Chunxia Zhang and Jiangshe Zhang},
  journal={ArXiv},
  year={2020},
  volume={abs/1901.00140}
}
Abstract Low-rank matrix factorization (LRMF) has received much popularity owing to its successful applications in both computer vision and data mining. By assuming noise to come from a Gaussian, Laplace or mixture of Gaussian distributions, significant efforts have been made on optimizing the (weighted) L1 or L2-norm loss between an observed matrix and its bilinear factorization. However, the type of noise distribution is generally unknown in real applications and inappropriate assumptions… Expand
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