Estimation and Clustering with Infinite Rankings
Machine Learning
2012-06-18 v1 Machine Learning
Abstract
This paper presents a natural extension of stagewise ranking to the the case of infinitely many items. We introduce the infinite generalized Mallows model (IGM), describe its properties and give procedures to estimate it from data. For estimation of multimodal distributions we introduce the Exponential-Blurring-Mean-Shift nonparametric clustering algorithm. The experiments highlight the properties of the new model and demonstrate that infinite models can be simple, elegant and practical.
Keywords
Cite
@article{arxiv.1206.3270,
title = {Estimation and Clustering with Infinite Rankings},
author = {Marina Meila and Le Bao},
journal= {arXiv preprint arXiv:1206.3270},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI2008)