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Knowledge Discovery from Layered Neural Networks Based on Non-negative Task Matrix Decomposition Chihiro WATANABE Kaoru HIRAMATSU Kunio KASHINO Publication IEICE TRANSACTIONS on Information and Systems Vol.E103-D No.2 pp.390-397 Publication Date: 2020/02/01 Publicized: 2019/10/23 Online ISSN: 1745-1361 DOI: 10.1587/transinf.2019EDP7136 Type of Manuscript: PAPER Category: Artificial Intelligence, Data Mining Keyword: interpretable machine learning, neural networks, non-negative matrix factorization, clustering, Full Text: PDF(1.4MB)>>
Summary: 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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