Sampling approach to sparse approximation problem: determining degrees of freedom by simulated annealing
Abstract
The approximation of a high-dimensional vector by a small combination of column vectors selected from a fixed matrix has been actively debated in several different disciplines. In this paper, a sampling approach based on the Monte Carlo method is presented as an efficient solver for such problems. Especially, the use of simulated annealing (SA), a metaheuristic optimization algorithm, for determining degrees of freedom (the number of used columns) by cross validation is focused on and tested. Test on a synthetic model indicates that our SA-based approach can find a nearly optimal solution for the approximation problem and, when combined with the CV framework, it can optimize the generalization ability. Its utility is also confirmed by application to a real-world supernova data set.
Cite
@article{arxiv.1603.01399,
title = {Sampling approach to sparse approximation problem: determining degrees of freedom by simulated annealing},
author = {Tomoyuki Obuchi and Yoshiyuki Kabashima},
journal= {arXiv preprint arXiv:1603.01399},
year = {2016}
}
Comments
5 pages, 3 figures, Proceedings of Eusipco 2016