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17 September 2005 Greedy adaptive discrimination: component analysis by simultaneous sparse approximation
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Proceedings Volume 5914, Wavelets XI; 59141R (2005)
Event: Optics and Photonics 2005, 2005, San Diego, California, United States
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.
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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);

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