A Gaussian Process Model for Data Association and a Semidefinite Programming Solution
Miguel Lázaro-Gredilla, Steven Van Vaerenbergh · IEEE Transactions on Neural Networks and Learning Systems · 2014
In this paper, we propose a Bayesian model for the data association problem, in which trajectory smoothness is enforced through the use of Gaussian process priors. This model allows to score candidate associations using the evidence framework, thus casting the data association problem into an optimization problem. Under some additional mild assumptions, this optimization problem is shown to be equivalent to a constrained Max K-section problem. Furthermore, for K=2, a MaxCut formulation is obtained, to which an approximate solution can be efficiently found using an SDP relaxation. Solving this MaxCut problem is equivalent to finding the optimal association out of the combinatorially many possibilities. The obtained clustering depends only on two hyperparameters, which can also be selected by maximum evidence.