A Geometric Analysis of Subspace Clustering with Outliers

Details A Geometric Analysis of Subspace Clustering with Outliers A Geometric Analysis of Subspace Clustering with Outliers

2011-12-19

arxiv.org/abs/1112.4258v2

Computer Science

Information Theory

Scientific paper

This paper considers the problem of clustering a collection of unlabeled data points assumed to lie near a union of lower dimensional planes. As is common in computer vision or unsupervised learning applications, we do not know in advance how many subspaces there are nor do we have any information about their dimensions. We develop a novel geometric analysis of an algorithm named Sparse Subspace Clustering (SSC [Elhamifar, 09]), which significantly broadens the range of problems where it is provably effective. For instance, we show that SSC can recover multiple subspaces, each of dimension comparable to the ambient dimension. We also prove that SSC can correctly cluster data points even when the subspaces of interest intersect. Further, we develop an extension of SSC that succeeds when the data set is corrupted with possibly overwhelmingly many outliers. Underlying our analysis are clear geometric insights, which may bear on other sparse recovery problems. A numerical study complements our theoretical analysis and demonstrates the effectiveness of these methods.

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