115 citations · 289 across the 7 of their papers we have counts for
7 papers
Principal Component Pursuit with Reduced Linear Measurements
Arvind Ganesh, Kerui Min, John Wright +1
In this paper, we study the problem of decomposing a superposition of a low-rank matrix and a sparse matrix when a relatively few linear measurements are available. This problem ar…
Compressive Principal Component Pursuit
John Wright, Arvind Ganesh, Kerui Min +1
We consider the problem of recovering a target matrix that is a superposition of low-rank and sparse components, from a small set of linear measurements. This problem arises in com…
Sparsity and Robustness in Face Recognition
John Wright, Arvind Ganesh, Allen Yang +2
This report concerns the use of techniques for sparse signal representation and sparse error correction for automatic face recognition. Much of the recent interest in these techniq…
TILT: Transform Invariant Low-rank Textures
Zhengdong Zhang, Arvind Ganesh, Xiao Liang +1
In this paper, we show how to efficiently and effectively extract a class of "low-rank textures" in a 3D scene from 2D images despite significant corruptions and warping. The low-r…
Fast L1-Minimization Algorithms For Robust Face Recognition
Allen Y. Yang, Zihan Zhou, Arvind Ganesh +2
L1-minimization refers to finding the minimum L1-norm solution to an underdetermined linear system b=Ax. Under certain conditions as described in compressive sensing theory, the mi…
Dense Error Correction for Low-Rank Matrices via Principal Component Pursuit
Arvind Ganesh, John Wright, Xiaodong Li +2
We consider the problem of recovering a low-rank matrix when some of its entries, whose locations are not known a priori, are corrupted by errors of arbitrarily large magnitude. It…