{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00216512","sets":["1164:3616:10863:10864"]},"path":["10864"],"owner":"44499","recid":"216512","title":["ストリームデータの部分空間クラスタリングにシーケンスデータの特徴を反映させた方法の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-02-18"},"_buckets":{"deposit":"efb1fc28-3e54-43c5-98ea-46cc09b643e0"},"_deposit":{"id":"216512","pid":{"type":"depid","value":"216512","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ストリームデータの部分空間クラスタリングにシーケンスデータの特徴を反映させた方法の検討","author_link":["558922","558924","558923"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ストリームデータの部分空間クラスタリングにシーケンスデータの特徴を反映させた方法の検討"}]},"item_type_id":"4","publish_date":"2022-02-18","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"早稲田大学基幹理工学部情報通信学科"},{"subitem_text_value":"早稲田大学大学院基幹理工学研究科情報理工・通信専攻"},{"subitem_text_value":"早稲田大学基幹理工学部情報通信学科／早稲田大学大学院基幹理工学研究科情報理工・通信専攻"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Communications and Computer Engineering, School of Fundamental Science and Engineering, Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Department of Computer Science and Communications Engineering, Graduate School of Fundamental Science and Engineering, Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Department of Communications and Computer Engineering, School of Fundamental Science and Engineering, Waseda University / Department of Computer Science and Communications Engineering, Graduate School of Fundamental Science and Engineering, Waseda University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"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/216512/files/IPSJ-AVM22116013.pdf","label":"IPSJ-AVM22116013.pdf"},"date":[{"dateType":"Available","dateValue":"2024-02-18"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-AVM22116013.pdf","filesize":[{"value":"1.1 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":"27"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"5a5d1352-2b25-4df1-acf0-22d31bf837b0","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"大和田, 悠生"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"堀江, 光彦"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"笠井, 裕之"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10438399","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8582","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年，データをいくつかの部分空間に分類する部分空間クラスタリング (SC) が広く利用されている．その中でも，意味のある順序構造をもつシーケンスデータに特化し，その制約を組み込んだ手法が，性能の高さから注目を集めており，その代表例として OSC (Ordered Subspace Clustering) がある．ただし OSC は，計算量の二次関数的な増大のため，スケーラビリティに限界がある．ここで，データが随時新規に到着し，部分空間の構造が一定でないストリームデータについて考える．ストリーム中の構造の変化に厳密に対応できる手法はこれまでほとんど考案されてこなかったが，StreamSSC (Stream Sparse Subspace Clustering) は，部分空間を代表する集合を抽出し適宜更新することで，前述の課題に対応した．またその過程で，計算量の大幅な削減にも成功している．本稿では，時間情報の制約を考慮することでシーケンスデータに対応することが可能な StreamSSC の拡張手法について検討する．実際には，ストリームデータの多くがシーケンスデータの特徴を持つことを念頭に置いて，ストリームデータの枠組みの中で，OSC によるクラスタリングを考えることで，スケーラビリティの増大を目指す．数値実験から，提案手法が既存の OSC より計算時間の点で優っていることを示す．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"5","bibliographic_titles":[{"bibliographic_title":"研究報告オーディオビジュアル複合情報処理（AVM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-02-18","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"13","bibliographicVolumeNumber":"2022-AVM-116"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":216512,"updated":"2025-01-19T15:49:50.289426+00:00","links":{},"created":"2025-01-19T01:17:05.306160+00:00"}