Georgia Institute of Technology College of Sciences

One of the most fundamental steps in data analysis and dimensionality reduction consists of approximating a given dataset by a single low-dimensional subspace, which is classically achieved via Principal Component Analysis (PCA). However, in many applications, the data often lie near a union of low-dimensional subspaces, reflecting the multiple categories or classes a set of observations may belong to. In this talk we discuss the problem of clustering a collection of unlabeled data points assumed to lie near a union of lower dimensional planes. Simply stated the task is to assign each data point to a cluster so as to recover all the hidden subspaces. 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 present a novel geometric analysis of an algorithm named sparse subspace clustering (SSC), 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 show 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. We will also demonstrate the effectiveness of these methods by various numerical studies.