IEICE TRANSACTIONS on Information
Knowledge Discovery from Layered Neural Networks Based on Non-negative Task Matrix Decomposition
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.
- Publication
- IEICE TRANSACTIONS on Information 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
Authors
Chihiro WATANABE
NTT Communication Science Laboratories
Kaoru HIRAMATSU
NTT Geospace Corporation, NEXTSITE Asakusa Building
Kunio KASHINO
NTT Communication Science Laboratories
Keyword
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Chihiro WATANABE, Kaoru HIRAMATSU, Kunio KASHINO, "Knowledge Discovery from Layered Neural Networks Based on Non-negative Task Matrix Decomposition" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 2, pp. 390-397, February 2020, doi: 10.1587/transinf.2019EDP7136.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7136/_p
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@ARTICLE{e103-d_2_390,
author={Chihiro WATANABE, Kaoru HIRAMATSU, Kunio KASHINO, },
journal={IEICE TRANSACTIONS on Information},
title={Knowledge Discovery from Layered Neural Networks Based on Non-negative Task Matrix Decomposition},
year={2020},
volume={E103-D},
number={2},
pages={390-397},
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.},
keywords={},
doi={10.1587/transinf.2019EDP7136},
ISSN={1745-1361},
month={February},}
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TY - JOUR
TI - Knowledge Discovery from Layered Neural Networks Based on Non-negative Task Matrix Decomposition
T2 - IEICE TRANSACTIONS on Information
SP - 390
EP - 397
AU - Chihiro WATANABE
AU - Kaoru HIRAMATSU
AU - Kunio KASHINO
PY - 2020
DO - 10.1587/transinf.2019EDP7136
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2020
AB - 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.
ER -
English
