Cart (Loading....) | Create Account
Close category search window

The Group Lasso for Stable Recovery of Block-Sparse Signal Representations

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Xiaolei Lv ; Div. of Inf. Eng., Nanyang Technol. Univ., Singapore, Singapore ; Guoan Bi ; Wan, C.

Group Lasso is a mixed l1/l2-regularization method for a block-wise sparse model that has attracted a lot of interests in statistics, machine learning, and data mining. This paper establishes the possibility of stably recovering original signals from the noisy data using the adaptive group Lasso with a combination of sufficient block-sparsity and favorable block structure of the overcomplete dictionary. The corresponding theoretical results about the solution uniqueness, support recovery and representation error bound are derived based on the properties of block-coherence and subcoherence. Compared with the theoretical results on the parametrized quadratic program of conventional sparse representation, our stability results are more general. A comparison with block-based orthogonal greedy algorithm is also presented. Numerical experiments demonstrate the validity and correctness of theoretical derivation and also show that in case of noisy situation, the adaptive group Lasso has a better reconstruction performance than the quadratic program approach if the observed sparse signals have a natural block structure.

Published in:

Signal Processing, IEEE Transactions on  (Volume:59 ,  Issue: 4 )

Date of Publication:

April 2011

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.