A deep neural network based quasi-linear kernel for support vector machines

Weite Li, Bo Zhou, Benhui Chen, Takayuki Furuzuki

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

This paper proposes a deep quasi-linear kernel for support vector machines (SVMs). The deep quasi-linear kernel can be constructed by using a pre-trained deep neural network. To realize this goal, a multilayer gated bilinear classifier is first designed to mimic the functionality of the pre-trained deep neural network, by generating the gate control signals using the deep neural network. Then, a deep quasi-linear kernel is derived by applying an SVM formulation to the multilayer gated bilinear classifier. In this way, we are able to further implicitly optimize the parameters of the multilayer gated bilinear classifier, which are a set of duplicate but independent parameters of the pre-trained deep neural network, by using an SVM optimization. Experimental results on different data sets show that SVMs with the proposed deep quasi-linear kernel have an ability to take advantage of the pre-trained deep neural networks and outperform SVMs with RBF kernels.

Original languageEnglish
Pages (from-to)2558-2565
Number of pages8
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE99A
Issue number12
DOIs
Publication statusPublished - 2016 Dec 1

Keywords

  • Data-dependent kernel
  • Deep neural network
  • Multilayer gated bilinear classifier
  • Support vector machine

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics
  • Electrical and Electronic Engineering

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