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International Conference on Engineering Applications of Neural Networks

EANN 2016: Engineering Applications of Neural Networks pp 309–316Cite as

Simple and Stable Internal Representation by Potential Mutual Information Maximization

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 629))

Abstract

The present paper aims to interpret final representations obtained by neural networks by maximizing the mutual information between neurons and data sets. Because complex procedures are needed to maximize information, the computational procedures are simplified as much as possible using the present method. The simplification lies in realizing mutual information maximization indirectly by focusing on the potentiality of neurons. The method was applied to restaurant data for which the ordinary regression analysis could not show good performance. For this problem, we tried to interpret final representations and obtain improved generalization performance. The results revealed a simple configuration where just a single important feature was extracted to explicitly explain the motivation to visit the restaurant.

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Author information

Authors and Affiliations

  1. IT Education Center and Graduate School of Science and Technology, Tokai Univerisity, 4-1-1 Kitakaname, Hiratsuka, Kanagawa, 259-1292, Japan

    Ryotaro Kamimura

Authors
  1. Ryotaro Kamimura
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Correspondence to Ryotaro Kamimura .

Editor information

Editors and Affiliations

  1. Robert Gordon University, Aberdeen, United Kingdom

    Chrisina Jayne

  2. Lab of Forest Informatics (FiLAB), Democritus University of Thrace Lab of Forest Informatics (FiLAB), Orestiada, Greece

    Lazaros Iliadis

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© 2016 Springer International Publishing Switzerland

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Kamimura, R. (2016). Simple and Stable Internal Representation by Potential Mutual Information Maximization. In: Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2016. Communications in Computer and Information Science, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-319-44188-7_23

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  • DOI: https://doi.org/10.1007/978-3-319-44188-7_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44187-0

  • Online ISBN: 978-3-319-44188-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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