A Gaussian Process Model for Data Association and a Semi-Definite Programming Solution

TitleA Gaussian Process Model for Data Association and a Semi-Definite Programming Solution
Publication TypeJournal Article
Year of Publication2014
AuthorsM. Lázaro-Gredilla, and S. Van Vaerenbergh
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume25
Issue11
Pagination1967-1979
Month PublishedNovember
ISSN2162-237X
AbstractIn 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 by 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.
DOI10.1109/TNNLS.2014.2300701
PDF version: