Hierarchical Gaussian Processes model for multi-task learning

作者：

Highlights：

• A Hierarchical Gaussian Process Multi-task Learning (HGPMT) method.

• Effectively utilizing the explicit correlation prior information among tasks.

• A much lower computational complexity than the cross-covariance-based methods.

• A multi-kernel learning method for learning non-stationary function.

• Experiment on both toy and real-world datasets for demonstrating its superiority.

摘要

•A Hierarchical Gaussian Process Multi-task Learning (HGPMT) method.•Effectively utilizing the explicit correlation prior information among tasks.•A much lower computational complexity than the cross-covariance-based methods.•A multi-kernel learning method for learning non-stationary function.•Experiment on both toy and real-world datasets for demonstrating its superiority.

论文关键词：GP-LVM,Multi-task learning,Feature learning,Hierarchical model

论文评审过程：Received 12 February 2017, Revised 31 August 2017, Accepted 12 September 2017, Available online 13 September 2017, Version of Record 21 September 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.09.021