Robust L/sub 1/ norm factorization in the presence of outliers and missing data by alternative convex programming

@article{Ke2005RobustL1,
  title={Robust L/sub 1/ norm factorization in the presence of outliers and missing data by alternative convex programming},
  author={Q. Ke and T. Kanade},
  journal={2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)},
  year={2005},
  volume={1},
  pages={739-746 vol. 1}
}
  • Q. Ke, T. Kanade
  • Published 2005
  • Mathematics, Computer Science
  • 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
Matrix factorization has many applications in computer vision. Singular value decomposition (SVD) is the standard algorithm for factorization. When there are outliers and missing data, which often happen in real measurements, SVD is no longer applicable. For robustness iteratively re-weighted least squares (IRLS) is often used for factorization by assigning a weight to each element in the measurements. Because it uses L/sub 2/ norm, good initialization in IRLS is critical for success, but is… Expand
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