Near-Oracle Performance of Greedy Block-Sparse Estimation Techniques from Noisy Measurements

Details Near-Oracle Performance of Greedy Block-Sparse Estimation Techniques from Noisy Measurements Near-Oracle Performance of Greedy Block-Sparse Estimation Techniques from Noisy Measurements

2010-09-05

arxiv.org/abs/1009.0906v1

Computer Science

Information Theory

15 pages, 2 figures. Submitted to IEEE J. Selected Topics in Signal Processing

Scientific paper

This paper examines the ability of greedy algorithms to estimate a block sparse parameter vector from noisy measurements. In particular, block sparse versions of the orthogonal matching pursuit and thresholding algorithms are analyzed under both adversarial and Gaussian noise models. In the adversarial setting, it is shown that estimation accuracy comes within a constant factor of the noise power. Under Gaussian noise, the Cramer-Rao bound is derived, and it is shown that the greedy techniques come close to this bound at high SNR. The guarantees are numerically compared with the actual performance of block and non-block algorithms, highlighting the advantages of block sparse techniques.

Affiliated with

Also associated with

No associations

Rating

Near-Oracle Performance of Greedy Block-Sparse Estimation Techniques from Noisy Measurements does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Near-Oracle Performance of Greedy Block-Sparse Estimation Techniques from Noisy Measurements, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Near-Oracle Performance of Greedy Block-Sparse Estimation Techniques from Noisy Measurements will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-473912