Machine Learning Department
School of Computer Science, Carnegie Mellon University


Inference for Distributions over the
Permutation Group

Jonathan Huang, Carlos Guestrin, Leonidas Guibas*

May 2008


Keywords: Identify management, permutations, approximate inference, group theoretical methods, sensor networks

Permutations are ubiquitous in many real-world problems, such as voting, ranking, and data association. Representing uncertainty over permutations is challenging, since there are n! possibilities, and typical compact and factorized probability distribution representations, such as graphical models, cannot capture the mutual exclusivity Constraints associated with permutations. In this paper, we use the low-frequency terms of a Fourier decomposition to represent distributions over permutations compactly. We present Kronecker conditioning, a new general and efficient approach for maintaining and updating these distributions directly in the Fourier domain. Low order Fourier-based approximations, however, may lead to functions that do not correspond to valid distributions. To address this problem, we present an efficient quadratic program defined directly in the Fourier domain for projection the approximation onto a relaxation of the polytope of legal marginal distributions. We demonstrate the effectiveness of our approach on a real camera-based multi-person tracking scenario.

31 pages

*Computer Science Department, Stanford University

SCS Technical Report Collection
School of Computer Science homepage

This page maintained by