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

Asymmetric kernel in Gaussian Processes for learning target variance