Corpus ID: 2072669

Theoretical Analysis of Sparse Subspace Clustering with Missing Entries

@inproceedings{Tsakiris2018TheoreticalAO,
  title={Theoretical Analysis of Sparse Subspace Clustering with Missing Entries},
  author={Manolis C. Tsakiris and Ren{\'e} Vidal},
  booktitle={ICML},
  year={2018}
}
  • Manolis C. Tsakiris, René Vidal
  • Published in ICML 2018
  • Mathematics, Computer Science
  • Sparse Subspace Clustering (SSC) is a popular unsupervised machine learning method for clustering data lying close to a union of low-dimensional linear subspaces; a problem with numerous applications in pattern recognition and computer vision. Even though the behavior of SSC for uncorrupted data is by now well-understood, little is known about its theoretical properties when applied to data with missing entries. In this paper we give the first interpretable theoretical guarantees for SSC with… CONTINUE READING

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    Citations

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    On Geometric Analysis of Affine Sparse Subspace Clustering

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    Efficient Solvers for Sparse Subspace Clustering

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    Evolutionary Self-Expressive Models for Subspace Clustering

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    Is an Affine Constraint Needed for Affine Subspace Clustering?

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