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

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.

Presented at:
IEEE Int. Conf. on Acoustics, Speech & Signal Processing (ICASSP), 2008, Las Vegas, Nevada, 30.03.2008 - 05.04.2008

Note: The status of this file is: Anyone

 Record created 2008-01-25, last modified 2020-10-25

Download fulltextPDF
External link:
Download fulltextURL
Rate this document:

Rate this document:
(Not yet reviewed)