Home Page

Papers

Submissions

News

Editorial Board

Announcements

Proceedings

Open Source Software

Search

Login



RSS Feed

The Kendall and Mallows Kernels for Permutations

Yunlong Jiao, Jean-Philippe Vert
Proceedings of The 32nd International Conference on Machine Learning, pp. 1935–1944, 2015

Abstract

We show that the widely used Kendall tau correlation coefficient is a positive definite kernel for permutations. It offers a computationally attractive alternative to more complex kernels on the symmetric group to learn from rankings, or to learn to rank. We show how to extend it to partial rankings or rankings with uncertainty, and demonstrate promising results on high-dimensional classification problems in biomedical applications.

Related Material