Dimension Reduction Using Nonnegative Matrix Tri-Factorization in Multi-label Classification

• Item type: SIG Technical Reports(1)
• 公開日: 2015-07-20
• タイトル: Dimension Reduction Using Nonnegative Matrix Tri-Factorization in Multi-label Classification
• タイトル / 言語: en
• タイトル: Dimension Reduction Using Nonnegative Matrix Tri-Factorization in Multi-label Classification
• 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: Graduate School of Information Science and Technology, Hokkaido University
• 著者所属: Graduate School of Information Science and Technology, Hokkaido University
• 著者所属: Graduate School of Information Science and Technology, Hokkaido University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Hokkaido University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Hokkaido University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Hokkaido University
• 著者名: Keigo, Kimura; Mineichi, Kudo; Lu, Sun
• 著者名(英): Keigo, Kimura; Mineichi, Kudo; Lu, Sun
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Multi-label classification problem has become more important in image processing and text analysis where an object often is associated with many labels at the same time. Recently, even in this problem setting dimension reduction aiming at avoiding the curse of dimensionality has gathered an attention, but it is still a challenging problem. Nonnegative Matrix Factorization (NMF) is one of promising ways for dimension reduction in unsupervised learning, and is extended from two-matrix factorization to triple-matrix factorization. In this paper, we reformulate the NMF with three factor matrices in such a way that it is solvable the problem of the combinatorial explosion of labels and incorporates the label correlation naturally in supervised learning. Experiments on web page classification datasets show the advantages of the proposed algorithm in the classification accuracy and computational time.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Multi-label classification problem has become more important in image processing and text analysis where an object often is associated with many labels at the same time. Recently, even in this problem setting dimension reduction aiming at avoiding the curse of dimensionality has gathered an attention, but it is still a challenging problem. Nonnegative Matrix Factorization (NMF) is one of promising ways for dimension reduction in unsupervised learning, and is extended from two-matrix factorization to triple-matrix factorization. In this paper, we reformulate the NMF with three factor matrices in such a way that it is solvable the problem of the combinatorial explosion of labels and incorporates the label correlation naturally in supervised learning. Experiments on web page classification datasets show the advantages of the proposed algorithm in the classification accuracy and computational time.
• 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 2015-MPS-104, 号 6, p. 1-4, 発行日 2015-07-20
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8833
• 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