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Title: Noisy sparse subspace clustering
Authors: Wang, Y.-X.
Xu, H. 
Issue Date: 2013
Citation: Wang, Y.-X.,Xu, H. (2013). Noisy sparse subspace clustering. 30th International Conference on Machine Learning, ICML 2013 (PART 1) : 89-97. ScholarBank@NUS Repository.
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 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. Copyright 2013 by the author(s).
Source Title: 30th International Conference on Machine Learning, ICML 2013
Appears in Collections:Staff Publications

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