Clustering of Nonnegative Data and an Application to Matrix Completion
Machine Learning
2020-09-04 v1 Machine Learning
Signal Processing
Abstract
In this paper, we propose a simple algorithm to cluster nonnegative data lying in disjoint subspaces. We analyze its performance in relation to a certain measure of correlation between said subspaces. We use our clustering algorithm to develop a matrix completion algorithm which can outperform standard matrix completion algorithms on data matrices satisfying certain natural conditions.
Cite
@article{arxiv.2009.01279,
title = {Clustering of Nonnegative Data and an Application to Matrix Completion},
author = {C. Strohmeier and D. Needell},
journal= {arXiv preprint arXiv:2009.01279},
year = {2020}
}