Asymmetric kernel in Gaussian Processes for learning target variance

S.L. Pintea*, J.C. van Gemert, A.W.M. Smeulders

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)70-77
Number of pages8
JournalPattern Recognition Letters
Publication statusPublished - 2018


  • Asymmetric kernel distances
  • Gaussian process
  • Kernel metric learning
  • Regression


Dive into the research topics of 'Asymmetric kernel in Gaussian Processes for learning target variance'. Together they form a unique fingerprint.

Cite this