Analysis of the Self Projected Matching Pursuit Algorithm
Computer Vision and Pattern Recognition
2020-06-09 v3 Information Theory
math.IT
Abstract
The convergence and numerical analysis of a low memory implementation of the Orthogonal Matching Pursuit greedy strategy, which is termed Self Projected Matching Pursuit, is presented. This approach renders an iterative way of solving the least squares problem with much less storage requirement than direct linear algebra techniques. Hence, it appropriate for solving large linear systems. The analysis highlights its suitability within the class of well posed problems.
Cite
@article{arxiv.1609.00053,
title = {Analysis of the Self Projected Matching Pursuit Algorithm},
author = {Laura Rebollo-Neira and Miroslav Rozloznik and Pradip Sasmal},
journal= {arXiv preprint arXiv:1609.00053},
year = {2020}
}
Comments
The routines for implementing the methods, as well as scripts to reproduce the examples in the manuscript, are available on the website: http://www.nonlinear-approx.info/examples/node04.html