Asymmetric kernel in Gaussian Processes for learning target variance

Journal Article (2018)
Author(s)

S. Pintea (TU Delft - Pattern Recognition and Bioinformatics)

Jan C. Gemert (TU Delft - Pattern Recognition and Bioinformatics)

Arnold W.M. Smeulders (Universiteit van Amsterdam)

Research Group
Pattern Recognition and Bioinformatics
DOI related publication
https://doi.org/10.1016/j.patrec.2018.02.026
More Info
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Publication Year
2018
Language
English
Research Group
Pattern Recognition and Bioinformatics
Volume number
108
Pages (from-to)
70-77

Abstract

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.

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