Structured Sparse Subspace Clustering: A unified optimization framework

@article{Li2015StructuredSS,
  title={Structured Sparse Subspace Clustering: A unified optimization framework},
  author={Chun-Guang Li and Ren{\'e} Vidal},
  journal={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2015},
  pages={277-286}
}
Subspace clustering refers to the problem of segmenting data drawn from a union of subspaces. State of the art approaches for solving this problem follow a two-stage approach. In the first step, an affinity matrix is learned from the data using sparse or low-rank minimization techniques. In the second step, the segmentation is found by applying spectral clustering to this affinity. While this approach has led to state of the art results in many applications, it is sub-optimal because it does… CONTINUE READING

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