Noisy Sparse Subspace Clustering

被引：0
|
作者：
Wang, Yu-Xiang [1 ]
Xu, Huan [2 ]
机构：
[1] Carnegie Mellon Univ, Machine Learning Dept, Pittsburgh, PA 15213 USA
[2] Natl Univ Singapore, Dept Mech Engn, Singapore 117576, Singapore
来源：
JOURNAL OF MACHINE LEARNING RESEARCH | 2016年 / 17卷
关键词：
Subspace clustering; robustness; stability; compressive sensing; sparse;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 unlabeled input data points, which are assumed to be 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 more practical settings and provides justification to the success of SSC in a class of real applications.
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页数：41
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