期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 40, 期 7, 页码 1755-1769出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2017.2719680
关键词
Kernel methods; permutation; Kendall tau correlation; Mallows model; cluster analysis of rank data; supervised classification of biomedical data
资金
- European Union 7th Framework Program through the Marie Curie ITNMLPM grant [316861]
- European Research Council [ERC-SMAC-280032]
- Miller Institute for Basic Research in Science
- Fulbright Foundation
We show that the widely used Kendall tau correlation coefficient, and the related Mallows kernel, are positive definite kernels for permutations. They offer computationally attractive alternatives to more complex kernels on the symmetric group to learn from rankings, or learn to rank. We show how to extend these kernels to partial rankings, multivariate rankings and uncertain rankings. Examples are presented on how to formulate typical problems of learning from rankings such that they can be solved with state-of-the-art kernel algorithms. We demonstrate promising results on clustering heterogeneous rank data and high-dimensional classification problems in biomedical applications.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据