Distributed Low-Rank Subspace Segmentation

  title={Distributed Low-Rank Subspace Segmentation},
  author={Ameet S. Talwalkar and Lester W. Mackey and Yadong Mu and Shih-Fu Chang and Michael I. Jordan},
  journal={2013 IEEE International Conference on Computer Vision},
Vision problems ranging from image clustering to motion segmentation to semi-supervised learning can naturally be framed as subspace segmentation problems, in which one aims to recover multiple low-dimensional subspaces from noisy and corrupted input data. Low-Rank Representation (LRR), a convex formulation of the subspace segmentation problem, is provably and empirically accurate on small problems but does not scale to the massive sizes of modern vision datasets. Moreover, past work aimed at… 

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