A Metric Learning Method for Improving Neural Network Based Kernel Learning for SVM

Peifeng Liang, Xueqin Yao, Jinglu Hu

研究成果: Conference contribution

抄録

A gated linear network is able to mimic the functionality of a pre-trained neural network with a compound activation function R(x) = x ∗ S(x). An SVM can then be formulated to further implicitly optimize the gated linear network, in which a quasi-linear kernel is composed by using the gate signal S(x) generated from the pre-trained neural network. In this way, we realize a neural network based kernel learning. In this paper, a distance metric learning is applied to improving the kernel learning. In the pre-training of neural network, the loss function of distance metric learning is used as a regularization term. With the loss function of distance metric learning, the samples from within-class become closer and that from between-class become farther, which can improve the quasi-linear kernel. Accordingly, the classifier optimized by SVM with quasi-linear kernel will have better performance. The proposed classification method is applied to different real-world datasets and simulation results confirm the effectiveness of the proposed method.

本文言語English
ホスト出版物のタイトルProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1641-1646
ページ数6
ISBN(電子版)9781538666500
DOI
出版ステータスPublished - 2019 1 16
イベント2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan
継続期間: 2018 10 72018 10 10

出版物シリーズ

名前Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
国/地域Japan
CityMiyazaki
Period18/10/718/10/10

ASJC Scopus subject areas

  • 情報システム
  • 情報システムおよび情報管理
  • 健康情報学
  • 人工知能
  • コンピュータ ネットワークおよび通信
  • 人間とコンピュータの相互作用

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