Compressive Principal Component Pursuit

1 code implementation • 21 Feb 2012 • John Wright, Arvind Ganesh, Kerui Min, Yi Ma

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.

Information Theory Information Theory

Sparsity and Robustness in Face Recognition

no code implementations • 3 Nov 2011 • John Wright, Arvind Ganesh, Allen Yang, Zihan Zhou, Yi Ma

This report concerns the use of techniques for sparse signal representation and sparse error correction for automatic face recognition.

Face Recognition Robust Face Recognition

Fast L1-Minimization Algorithms For Robust Face Recognition

no code implementations • 21 Jul 2010 • Allen Y. Yang, Zihan Zhou, Arvind Ganesh, S. Shankar Sastry, Yi Ma

L1-minimization refers to finding the minimum L1-norm solution to an underdetermined linear system b=Ax.

Compressive Sensing Face Recognition +1

Fast L1-Minimization Algorithms For Robust Face Recognition

1 code implementation • 21 Jul 2010 • Allen Y. Yang, Zihan Zhou, Arvind Ganesh, S. Shankar Sastry, Yi Ma

L1-minimization refers to finding the minimum L1-norm solution to an underdetermined linear system b=Ax.

Compressive Sensing Face Recognition +1

Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization

no code implementations • NeurIPS 2009 • John Wright, Arvind Ganesh, Shankar Rao, Yigang Peng, Yi Ma

Principal component analysis is a fundamental operation in computational data analysis, with myriad applications ranging from web search to bioinformatics to computer vision and image analysis.