FAµST v1.x (Matlab implementation)
The latest Matlab implementation of FAµST (v1.1) is available in the Software Heritage archive:
or directly on Inria’s gitlab.
It allows to reproduce the results of all the experiments realized in [1] and [2]
The Matlab version of FAµST is distributed under the terms of the GNU Affero General Public License.
How to cite:
Luc Le Magoarou (2017), Faµst v1.1. https://archive.softwareheritage.org/swh:1:rel:ee8e7f4de4e3520941b56d05cb66d48ecc7b9952;origin=https://gitlab.inria.fr/faustgrp/FAuST-1.x;visit=swh:1:snp:18866028b6e3d6a3634b9490592197869e0a2f09.
bibtex:
@software{lemagoarou2017faust,
title = {FAµST v1.1},
author = {Luc Le Magoarou},
year = {2017},
institution = {Inria},
license = {AGPL-3.0},
url = {https://gitlab.inria.fr/faustgrp/FAuST-1.x},
repository= {https://gitlab.inria.fr/faustgrp/FAuST-1.x},
Howpublished={\url{https://archive.softwareheritage.org/swh:1:rel:ee8e7f4de4e3520941b56d05cb66d48ecc7b9952;origin=https://gitlab.inria.fr/faustgrp/FAuST-1.x;visit=swh:1:snp:18866028b6e3d6a3634b9490592197869e0a2f09}},
}
[1] Luc Le Magoarou, Rémi Gribonval. Flexible Multi-layer Sparse Approximations of Matrices and Applications. IEEE Journal of Selected Topics in Signal Processing, 2016, 10 (4), pp.688-700.
[2] Luc Le Magoarou, Rémi Gribonval, Nicolas Tremblay. Approximate fast graph Fourier transforms via multi-layer sparse approximations. IEEE Transactions on Signal and Information Processing over Networks, 2018, 4 (2), pp.407–420.
