  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.

Machine Learning;Artificial Intelligence;Machine Learning