  Multi-output Gaussian processes have received increasing attention during the
last few years as a natural mechanism to extend the powerful flexibility of
Gaussian processes to the setup of multiple output variables. The key point
here is the ability to design kernel functions that allow exploiting the
correlations between the outputs while fulfilling the positive definiteness
requisite for the covariance function. Alternatives to construct these
covariance functions are the linear model of coregionalization and process
convolutions. Each of these methods demand the specification of the number of
latent Gaussian process used to build the covariance function for the outputs.
We propose in this paper, the use of an Indian Buffet process as a way to
perform model selection over the number of latent Gaussian processes. This type
of model is particularly important in the context of latent force models, where
the latent forces are associated to physical quantities like protein profiles
or latent forces in mechanical systems. We use variational inference to
estimate posterior distributions over the variables involved, and show examples
of the model performance over artificial data, a motion capture dataset, and a
gene expression dataset.
