Parallel MMF: a Multiresolution Approach to Matrix Computation
Numerical Analysis
2015-07-17 v1 Machine Learning
Machine Learning
Abstract
Multiresolution Matrix Factorization (MMF) was recently introduced as a method for finding multiscale structure and defining wavelets on graphs/matrices. In this paper we derive pMMF, a parallel algorithm for computing the MMF factorization. Empirically, the running time of pMMF scales linearly in the dimension for sparse matrices. We argue that this makes pMMF a valuable new computational primitive in its own right, and present experiments on using pMMF for two distinct purposes: compressing matrices and preconditioning large sparse linear systems.
Cite
@article{arxiv.1507.04396,
title = {Parallel MMF: a Multiresolution Approach to Matrix Computation},
author = {Risi Kondor and Nedelina Teneva and Pramod K. Mudrakarta},
journal= {arXiv preprint arXiv:1507.04396},
year = {2015}
}