{"updated":"2025-01-21T15:39:32.008184+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00091278","sets":["934:989:7128:7129"]},"path":["7129"],"owner":"11","recid":"91278","title":["SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations"],"pubdate":{"attribute_name":"公開日","attribute_value":"2013-03-12"},"_buckets":{"deposit":"8d395428-30e1-4ceb-95a9-765360d62c1d"},"_deposit":{"id":"91278","pid":{"type":"depid","value":"91278","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations"},{"subitem_title":"SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[オリジナル論文] Canonical correlation analysis, semi-supervised learning, generalized eigenproblem, principal component analysis, multi-label prediction","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2013-03-12","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"NTT Communication Science Laboratories, NTT Corporation"},{"subitem_text_value":"Graduate School of Information Science and Engineering, Tokyo Institute of Technology"},{"subitem_text_value":"NTT Communication Science Laboratories, NTT Corporation／Graduate School of Information Science and Technologies, the University of Tokyo"},{"subitem_text_value":"NTT Communication Science Laboratories, NTT Corporation／Graduate School of Information Science and Technologies, the University of Tokyo"},{"subitem_text_value":"NTT Communication Science Laboratories, NTT Corporation"},{"subitem_text_value":"NTT Communication Science Laboratories, NTT Corporation"},{"subitem_text_value":"NTT 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However, CCA tends to perform poorly when the number of paired samples is limited, which is often the case in practice. To cope with this problem, we propose a semi-supervised variant of CCA named SemiCCA that allows us to incorporate additional unpaired samples for mitigating overfitting. Advantages of the proposed method over previously proposed methods are its computational efficiency and intuitive operationality: it smoothly bridges the generalized eigenvalue problems of CCA and principal component analysis (PCA), and thus its solution can be computed efficiently just by solving a single eigenvalue problem as the original CCA.","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Canonical correlation analysis (CCA) is a powerful tool for analyzing multi-dimensional paired data. However, CCA tends to perform poorly when the number of paired samples is limited, which is often the case in practice. To cope with this problem, we propose a semi-supervised variant of CCA named SemiCCA that allows us to incorporate additional unpaired samples for mitigating overfitting. Advantages of the proposed method over previously proposed methods are its computational efficiency and intuitive operationality: it smoothly bridges the generalized eigenvalue problems of CCA and principal component analysis (PCA), and thus its solution can be computed efficiently just by solving a single eigenvalue problem as the original CCA.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"135","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌数理モデル化と応用（TOM）"}],"bibliographicPageStart":"128","bibliographicIssueDates":{"bibliographicIssueDate":"2013-03-12","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"6"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-18T23:40:35.203928+00:00","id":91278,"links":{}}