Noisy Sparse Subspace Clustering

ICML 2013 pp. 89-97

/icml/2013/wang2013icml-noisy/

Abstract

This paper considers the problem of subspace clustering under noise. Specifically, we study the behavior of Sparse Subspace Clustering (SSC) when either adversarial or random noise is added to the unlabelled input data points, which are assumed to lie in a union of low-dimensional subspaces. We show that a modified version of SSC is \emphprovably effective in correctly identifying the underlying subspaces, even with noisy data. This extends theoretical guarantee of this algorithm to the practical setting and provides justification to the success of SSC in a class of real applications.

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Cite

Text

Wang and Xu. "Noisy Sparse Subspace Clustering." International Conference on Machine Learning, 2013.

Markdown

[Wang and Xu. "Noisy Sparse Subspace Clustering." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/wang2013icml-noisy/)

BibTeX

@inproceedings{wang2013icml-noisy,
  title     = {{Noisy Sparse Subspace Clustering}},
  author    = {Wang, Yu-Xiang and Xu, Huan},
  booktitle = {International Conference on Machine Learning},
  year      = {2013},
  pages     = {89-97},
  volume    = {28},
  url       = {https://mlanthology.org/icml/2013/wang2013icml-noisy/}
}