Alignment Based Kernel Selection for Multi-Label Learning

Alignment Based Kernel Selection for Multi-Label Learning Neural Process Lett https://doi.org/10.1007/s11063-018-9863-z Alignment Based Kernel Selection for Multi-Label Learning 1 2 3 Linlin Chen · Degang Chen · Hui Wang © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Kernel based methods are increasingly being used for data modeling because of their conceptual simplicity and outstanding performance on many learning tasks. And kernel alignment, which is usually employed to select particular kernel for a learning task, is an effective quantity to measure the degree of agreement between a kernel and a learning task. However, the existing kernel alignment methods are usually developed for single-label classification problems. In this paper, we consider kernel alignment for multi-label learning to address the problem of kernel selection. Our basic idea is that, firstly an ideal kernel is presented in terms of multiple labels. Then kernel is selected by selecting the parameters of a linear combination of base kernels through maximizing the alignment value between the combined kernel and ideal kernel, and the selected kernel is employed in the binary relevance approach for multi-label learning to construct SVMs as classifiers. Furthermore, our proposed method is improved by considering local kernel alignment criterion. Our idea of selecting kernels by kernel alignment http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Processing Letters Springer Journals

Alignment Based Kernel Selection for Multi-Label Learning

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Complex Systems; Computational Intelligence
ISSN
1370-4621
eISSN
1573-773X
D.O.I.
10.1007/s11063-018-9863-z
Publisher site
See Article on Publisher Site

Abstract

Neural Process Lett https://doi.org/10.1007/s11063-018-9863-z Alignment Based Kernel Selection for Multi-Label Learning 1 2 3 Linlin Chen · Degang Chen · Hui Wang © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Kernel based methods are increasingly being used for data modeling because of their conceptual simplicity and outstanding performance on many learning tasks. And kernel alignment, which is usually employed to select particular kernel for a learning task, is an effective quantity to measure the degree of agreement between a kernel and a learning task. However, the existing kernel alignment methods are usually developed for single-label classification problems. In this paper, we consider kernel alignment for multi-label learning to address the problem of kernel selection. Our basic idea is that, firstly an ideal kernel is presented in terms of multiple labels. Then kernel is selected by selecting the parameters of a linear combination of base kernels through maximizing the alignment value between the combined kernel and ideal kernel, and the selected kernel is employed in the binary relevance approach for multi-label learning to construct SVMs as classifiers. Furthermore, our proposed method is improved by considering local kernel alignment criterion. Our idea of selecting kernels by kernel alignment

Journal

Neural Processing LettersSpringer Journals

Published: May 28, 2018

References

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