Enhanced lasso recovery on graph. Bresson, X., Laurent, T., & von Brecht , J. In 2015 23rd European Signal Processing Conference (EUSIPCO), pages 1501-1505, Aug, 2015.
Paper doi abstract bibtex This work aims at recovering signals that are sparse on graphs. Compressed sensing offers techniques for signal recovery from a few linear measurements and graph Fourier analysis provides a signal representation on graph. In this paper, we leverage these two frameworks to introduce a new Lasso recovery algorithm on graphs. More precisely, we present a non-convex, non-smooth algorithm that outperforms the standard convex Lasso technique. We carry out numerical experiments on three benchmark graph datasets.
@InProceedings{7362634,
author = {X. Bresson and T. Laurent and J. {von Brecht}},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
title = {Enhanced lasso recovery on graph},
year = {2015},
pages = {1501-1505},
abstract = {This work aims at recovering signals that are sparse on graphs. Compressed sensing offers techniques for signal recovery from a few linear measurements and graph Fourier analysis provides a signal representation on graph. In this paper, we leverage these two frameworks to introduce a new Lasso recovery algorithm on graphs. More precisely, we present a non-convex, non-smooth algorithm that outperforms the standard convex Lasso technique. We carry out numerical experiments on three benchmark graph datasets.},
keywords = {Fourier analysis;graph theory;signal representation;signal recovery;compressed sensing;signal representation;graph Fourier analysis;Lasso recovery algorithm;nonconvex nonsmooth algorithm;Signal processing algorithms;Standards;Signal processing;Europe;Algorithm design and analysis;Laplace equations;Convex functions;Graph spectral analysis;Fourier basis;Lasso;ℓ1 relaxation;sparse recovery;non-convex optimization},
doi = {10.1109/EUSIPCO.2015.7362634},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570096793.pdf},
}
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