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SoS-RSC: A Sum-of-Squares Polynomial Approach to Robustifying Subspace Clustering Algorithms

Mario Sznaier, Octavia Camps
CVPR 2018
doi:10.1109/CVPR.2018.00838 /cvpr/2018/sznaier2018cvpr-sosrsc/

Abstract

This paper addresses the problem of subspace clustering in the presence of outliers. Typically, this scenario is handled through a regularized optimization, whose computational complexity scales polynomially with the size of the data. Further, the regularization terms need to be manually tuned to achieve optimal performance. To circumvent these difficulties, in this paper we propose an outlier removal algorithm based on evaluating a suitable sum-ofsquares polynomial, computed directly from the data. This algorithm only requires performing two singular value decompositions of fixed size, and provides certificates on the probability of misclassifying outliers as inliers.

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Text

Sznaier and Camps. "SoS-RSC: A Sum-of-Squares Polynomial Approach to Robustifying Subspace Clustering Algorithms." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00838

Markdown

[Sznaier and Camps. "SoS-RSC: A Sum-of-Squares Polynomial Approach to Robustifying Subspace Clustering Algorithms." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/sznaier2018cvpr-sosrsc/) doi:10.1109/CVPR.2018.00838

BibTeX

@inproceedings{sznaier2018cvpr-sosrsc,
  title     = {{SoS-RSC: A Sum-of-Squares Polynomial Approach to Robustifying Subspace Clustering Algorithms}},
  author    = {Sznaier, Mario and Camps, Octavia},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2018},
  doi       = {10.1109/CVPR.2018.00838},
  url       = {https://mlanthology.org/cvpr/2018/sznaier2018cvpr-sosrsc/}
}