Multiple kernel based feature and decision level fusion of iECO individuals for explosive hazard detection in FLIR imagery

Price, Stanton R.; Murray, Bryce; Hu, Lequn; Anderson, Derek T.; Havens, Timothy C.; Luke, Robert H.; Keller, James M.

doi:10.1117/12.2223297

Cited by 3 publications

(1 citation statement)

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“…In [9,10], we started to explore the possibility of deriving the kernel weights (discussed in detail in Section 2), which dictate how a given set of kernels are combined and ultimately contribute to the task at hand, based on kernel matrix properties. That is, we do not make any connections between weight derivation and the underlying optimization task.…”

Section: Introductionmentioning

confidence: 99%

Kernel Matrix-Based Heuristic Multiple Kernel Learning

2022

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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.

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Section: Introductionmentioning

confidence: 99%