{"created":"2025-01-19T01:18:57.288061+00:00","updated":"2025-01-19T15:06:49.438426+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218599","sets":["1164:2735:10865:10962"]},"path":["10962"],"owner":"44499","recid":"218599","title":["HSICを拡張した条件付き独立検定"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-06-20"},"_buckets":{"deposit":"1426fb84-995e-4c2a-8cbb-e3e5801bb429"},"_deposit":{"id":"218599","pid":{"type":"depid","value":"218599","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"HSICを拡張した条件付き独立検定","author_link":["568906","568907","568905","568908"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"HSICを拡張した条件付き独立検定"},{"subitem_title":"Extending HSIC for Testing Conditional Independence","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2022-06-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"大阪大学基礎工学研究科"},{"subitem_text_value":"大阪大学基礎工学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Engineering Science, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering Science, Osaka University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/218599/files/IPSJ-MPS22138029.pdf","label":"IPSJ-MPS22138029.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS22138029.pdf","filesize":[{"value":"936.0 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"e211473e-df77-4729-a7b0-232b0f576c4c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 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":"張, 秉元"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"鈴木, 讓"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Bingyuan, Zhang","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Joe, Suzuki","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":"Conditional Independence (CI) testing is a fundamental problem in statistics, which is applied directly to causal　discovery. Many nonparametric CI tests are developed, but a common challenge exists: current methods perform poorly with a high dimensional conditioning set. To tackle this problem, we consider a novel nonparametric CI test using a kernel-based measure, which can be viewed as an extension of the Hilbert-Schmidt Independence Criterion (HSIC). The experimental results show that our proposed method leads to a significant performance improvement when compared with previous methods. In particular, our method performs well against the growth of the dimension of the conditioning set. Meanwhile, our method shows competitive scalability regarding the sample size ???? and the dimension of the conditioning set.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Conditional Independence (CI) testing is a fundamental problem in statistics, which is applied directly to causal　discovery. Many nonparametric CI tests are developed, but a common challenge exists: current methods perform poorly with a high dimensional conditioning set. To tackle this problem, we consider a novel nonparametric CI test using a kernel-based measure, which can be viewed as an extension of the Hilbert-Schmidt Independence Criterion (HSIC). The experimental results show that our proposed method leads to a significant performance improvement when compared with previous methods. In particular, our method performs well against the growth of the dimension of the conditioning set. Meanwhile, our method shows competitive scalability　regarding the sample size n and the dimension of the conditioning set.","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":"2022-06-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"29","bibliographicVolumeNumber":"2022-MPS-138"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":218599,"links":{}}