Average case analysis of sparse recovery with thresholding : New bounds based on average dictionary coherence

Mohammad Golbabaee, Pierre Vandergheynst

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

1 Citation (SciVal)

Abstract

This paper analyzes the performance of the simple thresholding algorithm for sparse signal representations. In particular, in order to be more realistic we introduce a new probabilistic signal model which assumes randomness for both the amplitude and also the location of nonzero entries. Based on this model we show that thresholding in average can correctly recover signals for much higher sparsity levels than was previously reported. The bounds we obtain in this paper are based on a new concept of average dictionary coherence and are shown to be much sharper than in former works [1,2].
Original languageEnglish
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherIEEE
ISBN (Print)978-1-4244-1483-3
DOIs
Publication statusPublished - 12 May 2008

Publication series

NameIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
ISSN (Print)1520-6149

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