Kernel Matrix-Based Heuristic Multiple Kernel Learning

Stanton R. Price, Derek T. Anderson, Timothy C. Havens and Steven R. Price
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Stanton R. Price: U.S. Army Engineer Research and Development Center, Geotechnical and Structures Laboratory, Vicksburg, MS 39180, USA
Derek T. Anderson: Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
Timothy C. Havens: Department of Electrical Engineering and Computer Science, College of Computing, Michigan Technological University, Houghton, MI 49931, USA
Steven R. Price: U.S. Army Engineer Research and Development Center, Geotechnical and Structures Laboratory, Vicksburg, MS 39180, USA

Mathematics, 2022, vol. 10, issue 12, 1-17

Abstract: Kernel theory is a demonstrated tool that has made its way into nearly all areas of machine learning. However, a serious limitation of kernel methods is knowing which kernel is needed in practice. Multiple kernel learning (MKL) is an attempt to learn a new tailored kernel through the aggregation of a set of valid known kernels. There are generally three approaches to MKL: fixed rules, heuristics, and optimization. Optimization is the most popular; however, a shortcoming of most optimization approaches is that they are tightly coupled with the underlying objective function and overfitting occurs. Herein, we take a different approach to MKL. Specifically, we explore different divergence measures on the values in the kernel matrices and in the reproducing kernel Hilbert space (RKHS). Experiments on benchmark datasets and a computer vision feature learning task in explosive hazard detection demonstrate the effectiveness and generalizability of our proposed methods.

Keywords: multiple kernel learning; divergence measures; heuristics; SVM (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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