Knowledge Discovery from Layered Neural Networks based on Non-negative Task Decomposition

Chihiro Watanabe; Kaoru Hiramatsu; Kunio Kashino

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arXiv · 1805.07137

Knowledge Discovery from Layered Neural Networks based on Non-negative Task Decomposition

Chihiro Watanabe·Kaoru Hiramatsu·Kunio Kashino

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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 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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BibTeX RIS

Chihiro Watanabe, Kaoru Hiramatsu, Kunio Kashino. 2018-05-21. Knowledge Discovery from Layered Neural Networks based on Non-negative Task Decomposition. https://arxiv.org/abs/1805.07137

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