Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction
Machine Learning
2014-06-12 v1
Abstract
This paper presents novel algorithms which exploit the intrinsic algebraic and combinatorial structure of the matrix completion task for estimating missing en- tries in the general low rank setting. For positive data, we achieve results out- performing the state of the art nuclear norm, both in accuracy and computational efficiency, in simulations and in the task of predicting athletic performance from partially observed data.
Keywords
Cite
@article{arxiv.1406.2864,
title = {Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction},
author = {Duncan A. J. Blythe and Louis Theran and Franz Kiraly},
journal= {arXiv preprint arXiv:1406.2864},
year = {2014}
}