A divide-and-conquer framework for large-scale subspace clustering

@article{You2016ADF,
  title={A divide-and-conquer framework for large-scale subspace clustering},
  author={Chong You and Claire Donnat and Daniel P. Robinson and Ren{\'e} Vidal},
  journal={2016 50th Asilomar Conference on Signals, Systems and Computers},
  year={2016},
  pages={1014-1018}
}
Given data that lies in a union of low-dimensional subspaces, the problem of subspace clustering aims to learn — in an unsupervised manner — the membership of the data to their respective subspaces. State-of-the-art subspace clustering methods typically adopt a two-step procedure. In the first step, an affinity measure among data points is constructed, usually by exploiting some form of data self-representation. In the second step, spectral clustering is applied to the affinity measure to find… CONTINUE READING

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