Noisy Sparse Subspace Clustering
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 be in a union of low-dimensional subspaces. We show that a modified version of SSC is \emph{provably effective} in correctly identifying the underlying subspaces, even with noisy data. This extends theoretical guarantee of this algorithm to more practical settings and provides justification to the success of SSC in a class of real applications.
Cite
@article{arxiv.1309.1233,
title = {Noisy Sparse Subspace Clustering},
author = {Yu-Xiang Wang and Huan Xu},
journal= {arXiv preprint arXiv:1309.1233},
year = {2015}
}
Comments
Manuscript currently under review at journal of machine learning research. Previously conference version appeared at ICML'12, and was uploaded to ArXiv by the conference committee