Gaussian kernel optimization for pattern classification

Abstract

This paper presents a novel algorithm to optimize the Gaussian kernel for pattern classification tasks, where it is desirable to have well-separated samples in the kernel feature space. We propose to optimize the Gaussian kernel parameters by maximizing a classical class separability criterion, and the problem is solved through a quasi-Newton algorithm by making use of a recently proposed decomposition of the objective criterion. The proposed method is evaluated on five data sets with two kernel-based learning algorithms. The experimental results indicate that it achieves the best overall classification performance, compared with three competing solutions. In particular, the proposed method provides a valuable kernel optimization solution in the severe small sample size scenario.

Publication
Pattern Recognition
Haiping Lu
Haiping Lu
Professor of Machine Learning, Head of AI Research Engineering, and Turing Academic Lead

I am a Professor of Machine Learning. I develop translational AI technologies for better analysing multimodal data in healthcare and beyond.