岩崎 亘 IWASAKI Toru

Articles:  1-9 of 9

  • An attempt of continuous latent variable model by non-negative kernel smoother  [in Japanese]

    石橋 英朗 , 岩崎 亘 , 渡辺 龍二 , 古川 徹生

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 117(325), 29-34, 2017-11-24

  • Kansei analysis of landscape images by Tensor SOM : Simultaneous analysis of evaluators, subjects, and evaluation words  [in Japanese]

    糸永 恭平 , 岩崎 亘 , 吉田 香 , 古川 徹生

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 117(109), 45-50, 2017-06-23

  • Robustness of Tensor SOM for Missing Data  [in Japanese]

    WAKITA Yasuhiro , IWASAKI Tohru , FURUKAWA Tetsuo

    Tensor SOM is an extension of the self-organizing map (SOM), which enables us to visualize simultaneous visualization of multiple modes of relational data. One of the typical applications is user-item …

    IEICE technical report. Neurocomputing 114(437), 21-26, 2015-01-29

  • Analysis of the MovieLens dataset using Tensor SOM  [in Japanese]

    DATE Yosuke , WAKITA Yasuhiro , IWASAKI Toru , FURUKAWA Tetsuo

    Tensor SOM is an extension of Self-Organizing Map (SOM) from vectorial data to tensorial ones. The aim of this paper is to apply Tensor SOM to the MovieLens dataset, which is a popular benchmark of re …

    IEICE technical report. Neurocomputing 113(500), 63-68, 2014-03-17

  • Individual visualization of lifestyle pattern by SOM^2 with class estimation  [in Japanese]

    ISHIBASHI Hideaki , IWASAKI Toru , HORIO Keiichi , NAMBA Hideyuki , FURUKAWA Tetsuo

    To decrease the risk of lifestyle-related deseases, it is important to recognize the life pattern such as exercise and sleep, and to improve it by oneself. For the purpose, life pattern visualization …

    IEICE technical report. Neurocomputing 113(148), 29-34, 2013-07-19

  • Tensor Decomposition using Self-Organizing Map and Missing Data Estimation  [in Japanese]

    HASHIMOTO Koji , IWASAKI Toni , FURUKAWA Tetsuo

    Tensor-Decomposition Self-Organizing Map (TD-SOM) is a nonlinear tensor decomposition method based on SOM. The aim of this research is to apply the TD-SOM to social network analysis. Since a social ne …

    IEICE technical report. Neurocomputing 112(390), 37-42, 2013-01-24

  • Tensor Decomposition using Self-Organizing Map and Missing Data Estimation  [in Japanese]

    HASHIMOTO Koji , IWASAKI Toni , FURUKAWA Tetsuo

    Tensor-Decomposition Self-Organizing Map (TD-SOM) is a nonlinear tensor decomposition method based on SOM. The aim of this research is to apply the TD-SOM to social network analysis. Since a social ne …

    IEICE technical report. Nonlinear problems 112(389), 37-42, 2013-01-24

  • Tensor Decomposition using Self-Organizing Map and Missing Data Estimation  [in Japanese]

    IWASAKI Toru , FURUKAWA Tetsuo

    The aim of this work is to develop a nonlinear tensor decomposition algorithm based on the self-organizing map (SOM), and to apply it to relational datasets. In this paper, we focus on the missing val …

    IEICE technical report. Neurocomputing 112(227), 55-60, 2012-09-27

    References (7)

  • Tensor decomposition based on self-organizing map  [in Japanese]

    IWASAKI Toru , WADA Saori , FURUKAWA Tetsuo

    The aim of this paper is to develop a nonlinear tensor decomposition algorithm based on the self-organiing map (SOM). The key ideas are (i) adopting the concept of the power of SOM (SOM^n), and (ii) r …

    IEICE technical report. Neurocomputing 111(419), 101-106, 2012-01-19

    References (9) Cited by (2)

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