IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532
Regular Section
Knowledge Discovery from Layered Neural Networks Based on Non-negative Task Matrix Decomposition
Chihiro WATANABEKaoru HIRAMATSUKunio KASHINO
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2020 Volume E103.D Issue 2 Pages 390-397

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Abstract

Interpretability has become an important issue in the machine learning field, along with the success of layered neural networks in various practical tasks. Since a trained layered neural network consists of a complex nonlinear relationship between large number of parameters, we failed to understand how they could achieve input-output mappings with a given data set. In this paper, we propose the non-negative task matrix decomposition method, which applies non-negative matrix factorization to a trained layered neural network. This enables us to decompose the inference mechanism of a trained layered neural network into multiple principal tasks of input-output mapping, and reveal the roles of hidden units in terms of their contribution to each principal task.

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© 2020 The Institute of Electronics, Information and Communication Engineers
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