ヒダカ アキノリ
HIDAKA Akinori
日高 章理 所属 東京電機大学 理工学部 理工学科 理学系 東京電機大学大学院 理工学研究科 理学専攻 東京電機大学大学院 先端科学技術研究科 数理学専攻 職種 准教授 |
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言語種別 | 英語 |
発行・発表の年月 | 2018/01 |
形態種別 | 学術研究論文 |
査読 | 査読有り |
標題 | Data Visualization for Deep Neural Networks Based on Interlayer Canonical Correlation Analysis |
執筆形態 | 共著 |
掲載誌名 | Transactions of ISCIE |
掲載区分 | 国内 |
出版社・発行元 | The Institute of Systems, Control and Information Engineers (ISCIE) |
巻・号・頁 | 31(1),pp.10-20 |
著者・共著者 | ◎Akinori Hidaka and Takio Kurita |
概要 | 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. |