Dense Error Correction via `-Minimization

Abstract

This paper studies the problem of recovering a sparse signal x ∈ R from highly corrupted linear measurements y = Ax + e ∈ R, where e is an unknown error vector whose nonzero entries may be unbounded. Motivated by an observation from face recognition in computer vision, this paper proves that for highly correlated (and possibly overcomplete) dictionaries A… (More)

Topics

7 Figures and Tables

Statistics

010202009201020112012201320142015201620172018
Citations per Year

65 Citations

Semantic Scholar estimates that this publication has 65 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Wright2008DenseEC, title={Dense Error Correction via `-Minimization}, author={John V. Wright}, year={2008} }