Greedy adaptive discrimination: component analysis by simultaneous sparse approximation

Jeffrey M. Sieracki; John J. Benedetto

doi:10.1117/12.626449

17 September 2005 Greedy adaptive discrimination: component analysis by simultaneous sparse approximation

Proceedings Volume 5914, Wavelets XI; 59141R (2005) https://doi.org/10.1117/12.626449
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

Abstract

Sparse approximation is typically concerned with generating compact representation of signals and data vectors by constructing a tailored linear combination of atoms drawn from a large dictionary. We have developed an algorithm based on simultaneous matching pursuits that facilitates the concurrent approximation of multiple signals in a common, low-dimensional representation space. The algorithm leads to an effective method of extracting signal components from collections of noisy data, and in particular is robust against jitter as well as additive noise. We illustrate its utility and compare performance in several variations by numerical examples.

Citation Download Citation

Jeffrey M. Sieracki and John J. Benedetto "Greedy adaptive discrimination: component analysis by simultaneous sparse approximation", Proc. SPIE 5914, Wavelets XI, 59141R (17 September 2005); https://doi.org/10.1117/12.626449

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available