Bayesian multiple and co-clustering methods: Application to fMRI data

• Item type: SIG Technical Reports(1)
• 公開日: 2014-07-15
• タイトル: Bayesian multiple and co-clustering methods: Application to fMRI data
• タイトル / 言語: en
• タイトル: Bayesian multiple and co-clustering methods: Application to fMRI data
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Okinawa Institute of Science and Technology Graduate University
• 著者所属: Okinawa Institute of Science and Technology Graduate University
• 著者所属: Okinawa Institute of Science and Technology Graduate University
• 著者所属: Kyoto University／Okinawa Institute of Science and Technology Graduate University
• 著者所属: Hiroshima University
• 著者所属: Hiroshima University
• 著者所属: Hiroshima University
• 著者所属: Hiroshima University
• 著者所属: Otemon Gakuin University
• 著者所属: Hiroshima University
• 著者所属: Hiroshima University
• 著者所属: Tokyo University
• 著者所属: Okinawa Institute of Science and Technology Graduate University
• 著者所属(英): en
• 著者所属(英): Okinawa Institute of Science and Technology Graduate University
• 著者所属(英): en
• 著者所属(英): Okinawa Institute of Science and Technology Graduate University
• 著者所属(英): en
• 著者所属(英): Okinawa Institute of Science and Technology Graduate University
• 著者所属(英): en
• 著者所属(英): Kyoto University / Okinawa Institute of Science and Technology Graduate University
• 著者所属(英): en
• 著者所属(英): Hiroshima University
• 著者所属(英): en
• 著者所属(英): Hiroshima University
• 著者所属(英): en
• 著者所属(英): Hiroshima University
• 著者所属(英): en
• 著者所属(英): Hiroshima University
• 著者所属(英): en
• 著者所属(英): Otemon Gakuin University
• 著者所属(英): en
• 著者所属(英): Hiroshima University
• 著者所属(英): en
• 著者所属(英): Hiroshima University
• 著者所属(英): en
• 著者所属(英): Tokyo University
• 著者所属(英): en
• 著者所属(英): Okinawa Institute of Science and Technology Graduate University
• 著者名: Tomoki, Tokuda; Junichiro, Yoshimoto; Yu, Shimizu; Kosuke, Yoshida; Shigeru, Toki; Go, Okada; Masahiro, Takamura; Tetsuya, Yamamoto; Shinpei, Yoshimura; Yasumasa, Okamoto; Shigeto, Yamawaki; Noriaki, Yahata; Kenji, Doya
• 著者名(英): Tomoki, Tokuda; Junichiro, Yoshimoto; Yu, Shimizu; Kosuke, Yoshida; Shigeru, Toki; Go, Okada; Masahiro, Takamura; Tetsuya, Yamamoto; Shinpei, Yoshimura; Yasumasa, Okamoto; Shigeto, Yamawaki; Noriaki, Yahata; Kenji, Doya
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: We propose a novel approach for the dimension reduction of high dimensional data to make the data available for conventional statistical evaluations. Our method is based on nonparametric multiple Gaussian clustering, in which we assume that in each cluster block, the instances follow an independent and identically (i.i.d.) univariate Gaussian distribution. We show theoretically that our model can fit multivariate Gaussian distributions with exchangeable features. We further show how the clusters derived with this specific model can be used to effectively reduce the dimension of data taking into account associations between attributes. Finally, we demonstrate our approach in an application to resting state functional magnetic resonance imaging (fMRI) data, which implies subtypes of depression may be characterized by the treatment effect of antidepressant drug SSRI.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: We propose a novel approach for the dimension reduction of high dimensional data to make the data available for conventional statistical evaluations. Our method is based on nonparametric multiple Gaussian clustering, in which we assume that in each cluster block, the instances follow an independent and identically (i.i.d.) univariate Gaussian distribution. We show theoretically that our model can fit multivariate Gaussian distributions with exchangeable features. We further show how the clusters derived with this specific model can be used to effectively reduce the dimension of data taking into account associations between attributes. Finally, we demonstrate our approach in an application to resting state functional magnetic resonance imaging (fMRI) data, which implies subtypes of depression may be characterized by the treatment effect of antidepressant drug SSRI.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11135936
• 書誌情報: 研究報告知能システム（ICS）; 巻 2014-ICS-176, 号 2, p. 1-5, 発行日 2014-07-15
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会