@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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