{"created":"2025-01-19T01:10:21.155672+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00209011","sets":["581:10433:10434"]},"path":["10434"],"owner":"44499","recid":"209011","title":["グラフを用いたNMFの地域分散高速化"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-01-15"},"_buckets":{"deposit":"4d2b8f32-b87c-4631-a45c-31d0183b83f4"},"_deposit":{"id":"209011","pid":{"type":"depid","value":"209011","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"グラフを用いたNMFの地域分散高速化","author_link":["525836","525835","525841","525838","525840","525837","525842","525839"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"グラフを用いたNMFの地域分散高速化"},{"subitem_title":"Graph-based Regional NMF for Distributed Computing","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[一般論文（推薦論文）] 行列分解，並列化，分散処理","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2021-01-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京理科大学理工学部情報科学科"},{"subitem_text_value":"日本電信電話株式会社NTTコミュニケーション科学基礎研究所／現在，京都大学大学院情報学研究科"},{"subitem_text_value":"日本電信電話株式会社NTTサービスエボリューション研究所／現在，株式会社ALBERT"},{"subitem_text_value":"日本電信電話株式会社NTTコミュニケーション科学基礎研究所"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Information Sciences, Tokyo University of Science","subitem_text_language":"en"},{"subitem_text_value":"NTT Communication Science Laboratories / Presently with Graduate School of Informatics, Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"NTT Service Evolution Laboratories / Presently with ALBERT Inc.","subitem_text_language":"en"},{"subitem_text_value":"NTT Communication Science Laboratories","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/209011/files/IPSJ-JNL6201046.pdf","label":"IPSJ-JNL6201046.pdf"},"date":[{"dateType":"Available","dateValue":"2023-01-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6201046.pdf","filesize":[{"value":"2.6 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"f6738bb9-f5d6-4e91-b2e1-94cbc014f7f7","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"越塚, 毅"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"竹内, 孝"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松林, 達史"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"澤田, 宏"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Koshizuka, Takeshi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Koh, Takeuchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tatsushi, Matsubayashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroshi, Sawada","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"集計データに対する教師なしのパターン認識技術として，Non-negative Matrix Factorization（NMF）は広く使われている．特にNon-negative Multiple Matrix Factorization（NMMF）では，複数のデータから共通する項目を共通因子として扱い，効果的に同時分解を行う．本研究では共通因子に加え，地域性などの物理的な関係性を持つ集計データに焦点を当て，因子分解を行うrNMF（regional Non-negative Matrix Factorization）を提案する．rNMFは，物理的に距離の近い地域のデータは同様の特徴空間で表現し，分析結果をより直感的に分かりやすいものとする．なお，地域の位置関係はグラフによって与える．さらに分析対象のデータが大規模な行列であっても，グラフの彩色問題をヒューリスティックに解くことで，分散システム上で高速に処理を行える．本稿では，まずrNMFをNMFの拡張として定式化を行い，パラメータ更新法も示す．地域ごとに集計された実データを用いた実験では，rNMFを用いることで，隣接した地域データを共通した特徴空間で表現できること，従来のNMFに対して汎化性能が悪化しないこと，分散システム上で高速に動作することを示す．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Non-negative Matrix Factorization (NMF) is a popular unsupervised pattern recognition technique for the analysis of aggregated data. In particular, Non-negative Multiple Matrix Factorization (NMMF) treats common elements from multiple data as common factors, and execute simultaneous decomposition effectively. In this study, we propose a novel matrix factorization method called regional Non-negative Matrix Factorization (rNMF), which factorises multiple matrics simultaneously, focusing on physical relation between aggregated data such as regional characteristics in addition to common factors. rNMF expresses data of physically close areas in a similar feature space, and extracts intuitively interpretable bases and coefficients from multiple matrics. The information of regional location is given by a graph. Furthermore, by solving the graph coloring problem heuristically, rNMF works at high speed on a distributed system even if the analyzed data are large matrices. In this paper, we formulate rNMF as an extended version of NMF and derive multiplicative update rules for parameter estimation. We performed experiment with real data, which were aggregated by region, in order to verify that rNMF can expresses adjacent regional data in a common feature, rNMF attained similar generalization performance as the original NMF, and rNMF works at high speed on a distributed system.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"396","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"387","bibliographicIssueDates":{"bibliographicIssueDate":"2021-01-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"62"}]},"relation_version_is_last":true,"item_2_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.20729/00208909","subitem_identifier_reg_type":"JaLC"}]},"weko_creator_id":"44499"},"id":209011,"updated":"2025-01-19T15:04:17.041674+00:00","links":{}}