{"created":"2025-01-19T01:02:02.380229+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00197604","sets":["1164:5352:9740:9828"]},"path":["9828"],"owner":"44499","recid":"197604","title":["LASSO推定量のスケーリングの下でのモデル選択について"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-06-10"},"_buckets":{"deposit":"bae13c50-ea32-414d-9069-90447d8c9ea7"},"_deposit":{"id":"197604","pid":{"type":"depid","value":"197604","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"LASSO推定量のスケーリングの下でのモデル選択について","author_link":["474379","474380"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"LASSO推定量のスケーリングの下でのモデル選択について"},{"subitem_title":"A model selection criterion for LASSO estimate with scaling","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2019-06-10","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"三重大学教育学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Faculty of Education","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/197604/files/IPSJ-BIO19058005.pdf","label":"IPSJ-BIO19058005.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO19058005.pdf","filesize":[{"value":"333.9 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"41"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"a3a76025-6b08-477c-8f44-8d93ce6b57da","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 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":"萩原, 克幸"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Katsuyuki, Hagiwara","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12055912","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":"2188-8590","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"LASSO (Least Absolute Shrinkage and Selection Operator) 推定量には過度のバイアスが生じるという問題があり，それを緩和するために，近年，非凸な正則化項の導入を含む様々な方法が提案されている．本研究では，その一つとして LASSO 推定量をスケーリングにより改良する方法を考えた．このスケーリングの度合いは過度の縮小を補正するという妥当性をもつだけでなく，LASSO 推定量に基づき簡単に計算することができるため，常に安定かつ高速な学習により LASSO のバイアス問題を解決する推定量を構成することができる．さらに，この推定量に対して，リスクの近似的な不偏推定量として，モデル選択規準を導出した．これが可能であることもこの推定量の利点の一つである．また，簡単な数値例により，この推定量の検証を行うとともに有効性を確認した．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"To relax a bias problem in LASSO (Least Absolute Shrinkage and Selection Operator), there have been several studies including the introduction of non-convex regularizers. In this article, we considered to improve a bias problem of LASSO estimator by scaling and derived a model selection criterion under the scaling method. The proposed scaling value is valid to compensate the excessive shrinkage of LASSO estimator and is easy to compute by using LASSO estimator. Moreover, we derived a model selection criterion based on a risk estimate. This analytic solution is also a benefit of the proposed scaling value. Furthermore, we verified the risk estimate and confirmed its effectiveness though a simple numerical example.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2019-06-10","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicVolumeNumber":"2019-BIO-58"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"links":{},"id":197604,"updated":"2025-01-19T22:17:17.367711+00:00"}