Sharp Thresholds for Sparsity Recovery in the Noisy and High-dimensional Setting Using l1-constrained Quadratic Programming

Wainwright, Martin J.

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https://hdl.handle.net/2142/131170 Copy

Description

Title

Sharp Thresholds for Sparsity Recovery in the Noisy and High-dimensional Setting Using l1-constrained Quadratic Programming

Author(s)

Wainwright, Martin J.

Issue Date

2006-09-27

Keyword(s)

• Quadratic programming
• Convex relaxation
• l0 minimization
• l1 relaxation
• Lasso
• Subset selection
• Consistency
• Thresholds
• Sparse approximation
• Signal denoising
• Sparsity recovery
• Model selection

Publisher

Allerton Conference on Communication, Control, and Computing

Series/Report Name or Number

2006 44th Allerton Conference on Communication, Control, and Computing Proceedings

ISSN

2836-4503

Type of Resource

Text

Genre of Resource

• Conference Paper
• Presentation

Language

eng

Handle URL

https://hdl.handle.net/2142/131170

Copyright and License Information

Copyright 2006 owned by the author(s).

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