{"created":"2025-01-19T00:36:19.474665+00:00","updated":"2025-01-20T10:51:14.153216+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00164518","sets":["1164:2735:8608:8756"]},"path":["8756"],"owner":"11","recid":"164518","title":["L0ノルム最適化手法に基づく高次元データの判別分析"],"pubdate":{"attribute_name":"公開日","attribute_value":"2016-06-27"},"_buckets":{"deposit":"5c0c520b-61ac-4309-9c0e-34e5f1bd9e41"},"_deposit":{"id":"164518","pid":{"type":"depid","value":"164518","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"L0ノルム最適化手法に基づく高次元データの判別分析","author_link":["324229","324227","324232","324231","324233","324230","324234","324228"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"L0ノルム最適化手法に基づく高次元データの判別分析"},{"subitem_title":"Classification analysis of high-dimensional data based on L0-norm optimization","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2016-06-27","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"電気通信大学大学院情報理工学研究科"},{"subitem_text_value":"電気通信大学大学院情報理工学研究科／日本学術振興会特別研究員"},{"subitem_text_value":"東京工業大学情報理工学院"},{"subitem_text_value":"電気通信大学先端領域教育研究センター"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"The University of Electro-Communications","subitem_text_language":"en"},{"subitem_text_value":"The University of Electro-Communications / Research Fellow for Young Scientists in Japan Society for the Promotion of Science","subitem_text_language":"en"},{"subitem_text_value":"Tokyo Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Center for Frontier Science and Engineering, The University of Electro-Communications","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/164518/files/IPSJ-MPS16108045.pdf","label":"IPSJ-MPS16108045.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS16108045.pdf","filesize":[{"value":"2.0 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"7d39c2f1-80ce-4ba3-afa2-83a0e84999c3","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2016 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"伊藤, 紀基"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"佐藤, 匡"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"樺島, 祥介"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"宮脇, 陽一"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Noriki, Ito","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masashi, Sato","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yoshiyuki, Kabashima","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yoichi, Miyawaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","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":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"計測技術の発達により，比較的容易に高次元データを得ることが可能になってきた．一方で，データが持つ高い次元に対して，データの性質を記述するのに十分なサンプル数が取れないという状況が，実験のコストをはじめとする様々な要因でしばしば生じうる．このような場合においてもデータの性質を信頼度高く記述しモデル化するためには，高次元データの中から真に重要な特徴量のみを抽出する特徴量選択のプロセスが極めて重要になる．高次元データからの適切な特徴量選択手法の実現を目指し，我々は選択する特徴量の個数を陽に制御する L0 ノルム最適化に基づく反復アルゴリズムに着目する．L0 ノルム最適化は，主として圧縮センシングの分野で研究が進んできているが，判別問題へと適用した例はこれまで明示的には提案されていない．そこで本研究では，iterative hard thresholding (IHT) に基づく L0 ノルム最適化手法を判別問題に適用する方法を提案し，その判別精度と特徴量選択の性能を評価した．シミュレーションの結果，提案手法は非スパース判別モデルである support vector machine (SVM) よりも判別精度が高く，スパース判別モデルである sparse logistic regression(SLR) よりも特徴量選択の精度が高い場合があることを確認した．これらの結果は，提案手法が高次元データからの効率的な特徴量抽出に貢献できる可能性があることを示唆している．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Advances in sensing devices allow us to measure high-dimensional data easily, but the sample size is often limited because of various reasons such as costs and duration to perform experiments. In such circumstances, feature selection plays a vital role to establish reliable models to describe characteristics of the high-dimensional data. For this purpose, we study iterative algorithms for L0-norm optimization that controls a number of features to be selected. The algorithms have been actively developed for compressed sensing, but not for classification problems explicitly. In this paper, we formulated a classification model with L0-norm regularization based on iterative hard thresholding (IHT) algorithm, quantified its performance in terms of accuracy in classification and feature selection, and compared the performance with that of representative models of a non-sparse classifier (support vector machine) and a sparse classifier (sparse logistic regression). Results showed that the IHT-based classifier outperformed the non-sparse classifier in terms of classification accuracy and did a sparse classifier in terms of feature selection accuracy for certain noise conditions. These results suggest that the proposed model serves an effective means to extract important features embedded in the high-dimensional data.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2016-06-27","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"45","bibliographicVolumeNumber":"2016-MPS-108"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":164518,"links":{}}