Gaussian process approach for metric learning

作者：

Highlights：

• propose a non-parametric metric learning approach (GP-Metric) based on Gaussian Process (GP).

• use GP to extend the bilinear similarity into a non-parametric form.

• develop an efficient algorithm to learn the non-parametric metric.

• demonstrate the performance of GP-Metric on real-world datasets.

摘要

•propose a non-parametric metric learning approach (GP-Metric) based on Gaussian Process (GP).•use GP to extend the bilinear similarity into a non-parametric form.•develop an efficient algorithm to learn the non-parametric metric.•demonstrate the performance of GP-Metric on real-world datasets.

论文关键词：Metric learning,Gaussian process,Bilinear similarity,Non-parametric metric

论文评审过程：Received 30 September 2017, Revised 8 August 2018, Accepted 9 October 2018, Available online 10 October 2018, Version of Record 13 October 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.10.010