Multiclass MinMax Rank Aggregation
Machine Learning
2017-02-06 v1 Artificial Intelligence
Quantitative Methods
Machine Learning
Abstract
We introduce a new family of minmax rank aggregation problems under two distance measures, the Kendall {\tau} and the Spearman footrule. As the problems are NP-hard, we proceed to describe a number of constant-approximation algorithms for solving them. We conclude with illustrative applications of the aggregation methods on the Mallows model and genomic data.
Cite
@article{arxiv.1701.08305,
title = {Multiclass MinMax Rank Aggregation},
author = {Pan Li and Olgica Milenkovic},
journal= {arXiv preprint arXiv:1701.08305},
year = {2017}
}