Deep hybrid neural-kernel networks using random Fourier features

Siamak Mehrkanoon*, Johan A. K. Suykens

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

17 Citations (Web of Science)

Abstract

This paper introduces a novel hybrid deep neural kernel framework. The proposed deep learning model makes a combination of a neural networks based architecture and a kernel based model. In particular, here an explicit feature map, based on random Fourier features, is used to make the transition between the two architectures more straightforward as well as making the model scalable to large datasets by solving the optimization problem in the primal. Furthermore, the introduced framework is considered as the first building block for the development of even deeper models and more advanced architectures. Experimental results show an improvement over shallow models and the standard non-hybrid neural networks architecture on several medium to large scale real-life datasets. (C) 2018 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)46-54
Number of pages9
JournalNeurocomputing
Volume298
DOIs
Publication statusPublished - 12 Jul 2018
Externally publishedYes

Keywords

  • Deep learning
  • Neural networks
  • Explicit feature mapping
  • Kernel methods
  • Hybrid models

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