Solving NMF with smoothness and sparsity constraints using PALM
Machine Learning
2021-03-19 v2 Computer Vision and Pattern Recognition
Machine Learning
Abstract
Non-negative matrix factorization is a problem of dimensionality reduction and source separation of data that has been widely used in many fields since it was studied in depth in 1999 by Lee and Seung, including in compression of data, document clustering, processing of audio spectrograms and astronomy. In this work we have adapted a minimization scheme for convex functions with non-differentiable constraints called PALM to solve the NMF problem with solutions that can be smooth and/or sparse, two properties frequently desired.
Cite
@article{arxiv.1910.14576,
title = {Solving NMF with smoothness and sparsity constraints using PALM},
author = {Raimon Fabregat and Nelly Pustelnik and Paulo Gonçalves and Pierre Borgnat},
journal= {arXiv preprint arXiv:1910.14576},
year = {2021}
}