Search Machine Learning Repository: Noisy Sparse Subspace Clustering
Authors: Yu-xiang Wang and Huan Xu
Conference: Proceedings of the 30th International Conference on Machine Learning (ICML-13)
Year: 2013
Pages: 89-97
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 \emph{provably 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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