Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
Papers
Data Visualization for Deep Neural Networks Based on Interlayer Canonical Correlation Analysis
Akinori HidakaTakio Kurita
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JOURNALS OPEN ACCESS

2018 Volume 31 Issue 1 Pages 10-20

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Abstract

In this paper, we develop data visualization methods which consider interlayer correlations in deep neural networks (DNN). In general, DNN naturally acquires multiple feature representations corresponding to their intermediate layers through their learning process. In order to understand relationships of those intermediate features which are strongly correlated with each other, we utilize canonical correlation analysis (CCA) to visualize the data distributions of different feature layers in a common subspace. Our method can grasp movement of samples between consecutive layers in DNN. By using standard benchmark data sets, we show that our visualization results contain much information that typical visualization methods (such as principal component analysis) usually do not represent.

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© 2018 The Institute of Systems, Control and Information Engineers
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