Bayesian Inference for Mixture of Sparse Linear Regression Model

• Item type: Trans(1)
• 公開日: 2021-08-10
• タイトル: Bayesian Inference for Mixture of Sparse Linear Regression Model
• タイトル / 言語: en
• タイトル: Bayesian Inference for Mixture of Sparse Linear Regression Model
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [オリジナル論文] Bayesian inference, mixture of sparse linear regression model, the exchange Monte Carlo method
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Frontier Science, The University of Tokyo
• 著者所属: Graduate School of Frontier Science, The University of Tokyo
• 著者所属: National Institute for Materials Science (NIMS)
• 著者所属: Research Center for Advanced Science and Technology, The University of Tokyo
• 著者所属: Graduate School of Engineering, The University of Tokyo
• 著者所属: Graduate School of Frontier Science, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Graduate School of Frontier Science, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Graduate School of Frontier Science, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): National Institute for Materials Science (NIMS)
• 著者所属(英): en
• 著者所属(英): Research Center for Advanced Science and Technology, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Graduate School of Frontier Science, The University of Tokyo
• 著者名: Tomoya, Hirakawa; Kyosuke, Matsudaira; Kenji, Nagata; Junya, Inoue; Manabu, Enoki; Masato, Okada
• 著者名(英): Tomoya, Hirakawa; Kyosuke, Matsudaira; Kenji, Nagata; Junya, Inoue; Manabu, Enoki; Masato, Okada
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: This paper proposes a Bayesian Inference for mixture of sparse linear regression models with the exchange Monte Carlo method. Mixture of linear regression model is a hybrid machine learning model that simultaneously performs clustering and linear regression. Mixture of sparse linear regression model imposes sparsity on the regression parameters and is expected to be applied to the analysis of real data in the field of materials science. The proposed method calculates the mixture ratio of each cluster, the label of each data point, and the posterior distribution of the sparse regression parameters by Bayesian inference using the exchange Monte Carlo method. Model selection based on the Bayesian free energy determines the appropriate number of mixtures of clusters. Experiments on artificial data confirmed that we obtained an appropriate posterior distribution of the parameters and showed appropriate model selection results. We applied our method to the data on aluminum alloys in materials science, and model selection and parameter estimation were performed by Bayesian inference.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: This paper proposes a Bayesian Inference for mixture of sparse linear regression models with the exchange Monte Carlo method. Mixture of linear regression model is a hybrid machine learning model that simultaneously performs clustering and linear regression. Mixture of sparse linear regression model imposes sparsity on the regression parameters and is expected to be applied to the analysis of real data in the field of materials science. The proposed method calculates the mixture ratio of each cluster, the label of each data point, and the posterior distribution of the sparse regression parameters by Bayesian inference using the exchange Monte Carlo method. Model selection based on the Bayesian free energy determines the appropriate number of mixtures of clusters. Experiments on artificial data confirmed that we obtained an appropriate posterior distribution of the parameters and showed appropriate model selection results. We applied our method to the data on aluminum alloys in materials science, and model selection and parameter estimation were performed by Bayesian inference.
• 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: AA11464803
• 書誌情報: 情報処理学会論文誌数理モデル化と応用（TOM）; 巻 14, 号 3, p. 93-101, 発行日 2021-08-10
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7780
• 出版者 / 言語: ja
• 出版者: 情報処理学会