Robust Generalized Low Rank Approximations of Matrices

@inproceedings{Shi2015RobustGL,
  title={Robust Generalized Low Rank Approximations of Matrices},
  author={Jiarong Shi and Wei Yang and Xiuyun Zheng and Fabio Rapallo},
  booktitle={PloS one},
  year={2015}
}
In recent years, the intrinsic low rank structure of some datasets has been extensively exploited to reduce dimensionality, remove noise and complete the missing entries. As a well-known technique for dimensionality reduction and data compression, Generalized Low Rank Approximations of Matrices (GLRAM) claims its superiority on computation time and compression ratio over the SVD. However, GLRAM is very sensitive to sparse large noise or outliers and its robust version does not have been… CONTINUE READING
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