FAuST Toolbox -- Flexible Approximate Multi-Layer Sparse Transform

General purpose

The FAuST toolbox contains a C++ code implementing a general framework designed to factorize matrices of interest into multiple sparse factors. It contains a template CPU/GPU C++ code and a Matlab wrapper. A Python wrapper is also available. The algorithms implemented here are described in details in [1]- Le Magoarou

For more information on the FAuST Project, please visit the website of the project: FAµST website

Installation

Please refer to the document "./gettingStartedFAuST-version2_0.pdf" to install the FAUST toolbox. The FAUST toolbox has been tested on the following environments:

• LINUX (fedora 20, 21, 22, 23, 24 - 27 / Ubuntu)
• MACOS X
• WINDOWS (windows 7)

Quick install on UNIX

Unpack the directory.
mkdir ./build
cd ./build
cmake .. OR ccmake .. (with Graphical User Interface)
make
make install

Warning: The Matlab interface of FAuST requires compiling mex files. The mex compiler compatible with specific versions of gcc depending on the platform used. For more information, please refer to the Mathworks website.

License

Copyright (2016): Luc Le Magoarou, Remi Gribonval, Nicolas Bellot, Adrien Leman, Thomas Gautrais INRIA Rennes, FRANCE http://www.inria.fr/

The FAuST Toolbox is distributed under the terms of the GNU Affero General Public License. This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see licenses.

Contacts

Rémi Gribonval: remi.gribonval@inria.fr
Hakim: hakim.hadj-djilani@inria.fr

Credits

Luc Le Magoarou
Remi Gribonval
Nicolas Bellot
Adrien Leman
Thomas Gautrais
Hakim H.

References

[1] Le Magoarou L. and Gribonval R., "Flexible multi-layer sparse approximations of matrices and applications", Journal of Selected Topics in Signal Processing, 2016.