This paper will serve as an introduction to the body of work on robust subspace recovery.
Robust subspace recovery involves finding an underlying low-dimensional subspace in a …

In this paper, we address the subspace clustering problem. Given a set of data samples
(vectors) approximately drawn from a union of multiple subspaces, our goal is to cluster the …

We propose low-rank representation (LRR) to segment data drawn from a union of multiple
linear (or affine) subspaces. Given a set of data vectors, LRR seeks the lowestrank …

Over the past few decades, significant progress has been made in clustering high-
dimensional data sets distributed around a collection of linear and affine subspaces. This …

We consider the problem of fitting a union of subspaces to a collection of data points drawn
from one or more subspaces and corrupted by noise and/or gross errors. We pose this …

Subspace clustering methods based on ell_1, l_2 or nuclear norm regularization have
become very popular due to their simplicity, theoretical guarantees and empirical success …

Subspace clustering refers to the problem of segmenting data drawn from a union of
subspaces. State-of-the-art approaches for solving this problem follow a two-stage …

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 …

Subspace clustering refers to the problem of segmenting data drawn from a union of
subspaces. State of the art approaches for solving this problem follow a two-stage approach …

In this paper, we study the problem of segmenting tracked feature point trajectories of
multiple moving objects in an image sequence. Using the affine camera model, this problem …