Overlapping Mixtures of Gaussian Processes for the data association problem

In this work we introduce a mixture of GPs to address the data association problem, i.e., to label a group of observations according to the sources that generated them. Unlike several previously proposed GP mixtures, the novel mixture has the distinct characteristic of using no gating function to determine the association of samples and mixture components. Instead, all the GPs in the mixture are global and samples are clustered following “trajectories” across input space. We use a non-standard variational Bayesian algorithm to efficiently recover sample labels and learn the hyperparameters. We show how multi-object tracking problems can be disambiguated and also explore the characteristics of the model in traditional regression settings.

► We use Gaussian Processes to label groups of samples according to the sources that generated them. ► Starting from a set of unlabeled samples, we can cluster them in natural “trajectories”. ► A variational approximation to exact Bayesian inference is provided. ► An enhanced variational bound is derived and used for hyper-parameter selection.