@inproceedings{3c0050ea87b947038c4904c58a04a090,
title = "Fast least squares pursuits for sparse recovery",
abstract = "We present a new greedy strategy with an efficient implementation technique that enjoys similar computational complexity to OMP. Its recovery performance in the noise free and the Gaussian noise cases is comparable and in many cases better than other existing sparse recovery algorithms both with respect to their theoretical guarantees and empirical reconstruction performance. Our framework has other appealing properties. Convergence is always guaranteed even in the case that the recovery conditions are violated. In addition, our implementation method is useful for improving the computational cost of other methods such as orthogonal least squares (OLS).",
author = "Guy Leibovitz and Raja Giryes",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 12th International Conference on Sampling Theory and Applications, SampTA 2017 ; Conference date: 03-07-2017 Through 07-07-2017",
year = "2017",
month = sep,
day = "1",
doi = "10.1109/SAMPTA.2017.8024418",
language = "الإنجليزيّة",
series = "2017 12th International Conference on Sampling Theory and Applications, SampTA 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "68--72",
editor = "Gholamreza Anbarjafari and Andi Kivinukk and Gert Tamberg",
booktitle = "2017 12th International Conference on Sampling Theory and Applications, SampTA 2017",
address = "الولايات المتّحدة",
}