Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation


Guangcan Liu, Huan Xu, Shuicheng Yan ;
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:703-711, 2012.


In this work, we address the following matrix recovery problem: suppose we are given a set of data points containing two parts, one part consists of samples drawn from a union of multiple subspaces and the other part consists of outliers. We do not know which data points are outliers, or how many outliers there are. The rank and number of the subspaces are unknown either. Can we detect the outliers and segment the samples into their right subspaces, efficiently and exactly? We utilize a so-called Low-Rank Representation (LRR) method to solve this problem, and prove that under mild technical conditions, any solution to LRR exactly recover the row space of the samples and detect the outliers as well. Since the subspace membership is provably determined by the row space, this further implies that LRR can perform exact subspace segmentation and outlier detection, in an efficient way.

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