A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data

@inproceedings{2015ADA,
  title={A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data},
  author={},
  year={2015}
}
  • Published 2015
Appendix A contains proofs for our main results. The proofs are sorted in the order that their corresponding statements appear in the paper. Appendix B formalizes our claims in the paper about attribute privacy and the corresponding utility theorem and includes additional discussions on the difficulty of a stronger user-level privacy claim. Appendix C contains numerical simulations on the performance of compressed SSC under fully random models. Appendix D summarizes a few concentration bounds… CONTINUE READING
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