  This work incorporates the multi-modality of the data distribution into a
Gaussian Process regression model. We approach the problem from a
discriminative perspective by learning, jointly over the training data, the
target space variance in the neighborhood of a certain sample through metric
learning. We start by using data centers rather than all training samples.
Subsequently, each center selects an individualized kernel metric. This enables
each center to adjust the kernel space in its vicinity in correspondence with
the topology of the targets --- a multi-modal approach. We additionally add
descriptiveness by allowing each center to learn a precision matrix. We
demonstrate empirically the reliability of the model.
