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• Weite LI

  Graduate School of Information, Production and Systems, Waseda University
  School of Electrical Engineering, University of Electronic Science and Technology of China

• Bo ZHOU

  Graduate School of Information, Production and Systems, Waseda University

• Benhui CHEN

  School of Mathematics and Computer Science, Dali University

• Jinglu HU

  Graduate School of Information, Production and Systems, Waseda University

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.