An exhaustive search and stability of sparse estimation for feature selection problem

• Item type: SIG Technical Reports(1)
• 公開日: 2014-09-18
• タイトル: An exhaustive search and stability of sparse estimation for feature selection problem
• タイトル / 言語: en
• タイトル: An exhaustive search and stability of sparse estimation for feature selection problem
• 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: The University of Tokyo
• 著者所属: Kobe University
• 著者所属: Nikon Corporation
• 著者所属: Fukushima Medical University
• 著者所属: the University of Toyama
• 著者所属: The University of Tokyo
• 著者所属(英): en
• 著者所属(英): The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Kobe University
• 著者所属(英): en
• 著者所属(英): Nikon Corporation
• 著者所属(英): en
• 著者所属(英): Fukushima Medical University
• 著者所属(英): en
• 著者所属(英): the University of Toyama
• 著者所属(英): en
• 著者所属(英): The University of Tokyo
• 著者名: Kenji, Nagata; Jun, Kitazono; Shin-ichiNakajima; Satoshi, Eifuku; Ryoi, Tamura; Masato, Okada
• 著者名(英): Kenji, Nagata; Jun, Kitazono; Shin-ichi, Nakajima; Satoshi, Eifuku; Ryoi, Tamura; Masato, Okada
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Feature selection problem has been widely used for various fields. In particular, the sparse estimation has the advantage that its computational cost is the polynomial order of the number of features. However, it has the problem that the obtained solution varies as the dataset has changed a little. The goal of this paper is to exhaustively search the solutions which minimize the generalization error for feature selection problem to investigate the problem of sparse estimation. We calculate the generalization errors for all combinations of features in order to get the histogram of generalization error by using the cross validation method. By using this histogram, we propose a method to verify whether the given data include information for binary classification by comparing the histogram of predictive error for random guessing. Moreover, we propose a statistical mechanical method in order to efficiently calculate the histogram of generalization error by the exchange Monte Carlo (EMC) method and the multiple histogram method. We apply our proposed method to the feature selection problem for selecting the relevant neurons for face identification.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Feature selection problem has been widely used for various fields. In particular, the sparse estimation has the advantage that its computational cost is the polynomial order of the number of features. However, it has the problem that the obtained solution varies as the dataset has changed a little. The goal of this paper is to exhaustively search the solutions which minimize the generalization error for feature selection problem to investigate the problem of sparse estimation. We calculate the generalization errors for all combinations of features in order to get the histogram of generalization error by using the cross validation method. By using this histogram, we propose a method to verify whether the given data include information for binary classification by comparing the histogram of predictive error for random guessing. Moreover, we propose a statistical mechanical method in order to efficiently calculate the histogram of generalization error by the exchange Monte Carlo (EMC) method and the multiple histogram method. We apply our proposed method to the feature selection problem for selecting the relevant neurons for face identification.
• 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: AN10505667
• 書誌情報: 研究報告数理モデル化と問題解決（MPS）; 巻 2014-MPS-100, 号 10, p. 1-6, 発行日 2014-09-18
• 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