Iterated geometric harmonics for data imputation and reconstruction of missing data
Machine Learning
2014-11-05 v1 Machine Learning
Abstract
The method of geometric harmonics is adapted to the situation of incomplete data by means of the iterated geometric harmonics (IGH) scheme. The method is tested on natural and synthetic data sets with 50--500 data points and dimensionality of 400--10,000. Experiments suggest that the algorithm converges to a near optimal solution within 4--6 iterations, at runtimes of less than 30 minutes on a medium-grade desktop computer. The imputation of missing data values is applied to collections of damaged images (suffering from data annihilation rates of up to 70\%) which are reconstructed with a surprising degree of accuracy.
Cite
@article{arxiv.1411.0997,
title = {Iterated geometric harmonics for data imputation and reconstruction of missing data},
author = {Chad Eckman and Jonathan A. Lindgren and Erin P. J. Pearse and David J. Sacco and Zachariah Zhang},
journal= {arXiv preprint arXiv:1411.0997},
year = {2014}
}
Comments
13 pages, 9 figures