{"created":"2023-12-18T01:16:09.454320+00:00","id":2000321,"links":{},"metadata":{"_buckets":{"deposit":"eb488d5d-3da3-44e4-a550-547de31041ac"},"_deposit":{"created_by":14,"id":"2000321","owner":"14","owners":[14],"pid":{"revision_id":0,"type":"depid","value":"2000321"},"status":"published"},"_oai":{"id":"oai:kyutech.repo.nii.ac.jp:02000321","sets":["8:24"]},"author_link":["27859","661"],"control_number":"2000321","item_21_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2022-12-17","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"157","bibliographicPageStart":"138","bibliographicVolumeNumber":"473","bibliographic_titles":[{"bibliographic_title":"Neurocomputing","bibliographic_titleLang":"en"}]}]},"item_21_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"In this study, we develop a method for multi-task manifold learning. The method aims to improve the performance of manifold learning for multiple tasks, particularly when each task has a small number of samples. Furthermore, the method also aims to generate new samples for new tasks, in addition to new samples for existing tasks. In the proposed method, we use two different types of information transfer: instance transfer and model transfer. For instance transfer, datasets are merged among similar tasks, whereas for model transfer, the manifold models are averaged among similar tasks. For this purpose, the proposed method consists of a set of generative manifold models corresponding to the tasks, which are integrated into a general model of a fiber bundle. We applied the proposed method to artificial datasets and face image sets, and the results showed that the method was able to estimate the manifolds, even for a tiny number of samples.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_21_link_62":{"attribute_name":"研究者情報","attribute_value_mlt":[{"subitem_link_url":"https://hyokadb02.jimu.kyutech.ac.jp/html/343_ja.html"}]},"item_21_publisher_7":{"attribute_name":"出版社","attribute_value_mlt":[{"subitem_publisher":"Elsevier"}]},"item_21_relation_12":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isVersionOf","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1016/j.neucom.2021.11.043","subitem_relation_type_select":"DOI"}}]},"item_21_rights_13":{"attribute_name":"著作権関連情報","attribute_value_mlt":[{"subitem_rights":"Copyright (c) 2021 Elsevier B. V. All rights reserved."}]},"item_21_select_59":{"attribute_name":"査読の有無","attribute_value_mlt":[{"subitem_select_item":"yes"}]},"item_21_source_id_10":{"attribute_name":"NCID","attribute_value_mlt":[{"subitem_source_identifier":"AA10827402","subitem_source_identifier_type":"NCID"}]},"item_21_source_id_8":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1872-8286","subitem_source_identifier_type":"EISSN"},{"subitem_source_identifier":"0925-2312","subitem_source_identifier_type":"PISSN"}]},"item_21_version_type_58":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_ab4af688f83e57aa","subitem_version_type":"AM"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Ishibashi, Hideaki","creatorNameLang":"en"},{"creatorName":"石橋, 英朗","creatorNameLang":"ja"}],"familyNames":[{"familyName":"Ishibashi","familyNameLang":"en"},{"familyName":"石橋","familyNameLang":"ja"}],"givenNames":[{"givenName":"Hideaki","givenNameLang":"en"},{"givenName":"英朗","givenNameLang":"ja"}],"nameIdentifiers":[{"nameIdentifier":"27859","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"30838389","nameIdentifierScheme":"e-Rad","nameIdentifierURI":"https://nrid.nii.ac.jp/ja/nrid/1000030838389"},{"nameIdentifier":"57191755812","nameIdentifierScheme":"Scopus著者ID","nameIdentifierURI":"https://www.scopus.com/authid/detail.uri?authorId=57191755812"},{"nameIdentifier":"100001227","nameIdentifierScheme":"九工大研究者情報","nameIdentifierURI":"https://hyokadb02.jimu.kyutech.ac.jp/html/##_ja.html"}]},{"creatorNames":[{"creatorName":"Higa, Kazushi","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Furukawa, Tetsuo","creatorNameLang":"en"},{"creatorName":"古川, 徹生","creatorNameLang":"ja"}],"familyNames":[{"familyName":"Furukawa","familyNameLang":"en"},{"familyName":"古川","familyNameLang":"ja"}],"givenNames":[{"givenName":"Tetsuo","givenNameLang":"en"},{"givenName":"徹生","givenNameLang":"ja"}],"nameIdentifiers":[{"nameIdentifier":"661","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"50219101","nameIdentifierScheme":"e-Rad","nameIdentifierURI":"https://nrid.nii.ac.jp/ja/nrid/1000050219101"},{"nameIdentifier":"56237975100","nameIdentifierScheme":"Scopus著者ID","nameIdentifierURI":"https://www.scopus.com/authid/detail.uri?authorId=56237975100"},{"nameIdentifier":"0000-0002-4469-7749","nameIdentifierScheme":"ORCiD","nameIdentifierURI":"https://orcid.org/0000-0002-4469-7749"},{"nameIdentifier":"343","nameIdentifierScheme":"九工大研究者情報","nameIdentifierURI":"https://hyokadb02.jimu.kyutech.ac.jp/html/##_ja.html"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2023-12-18"}],"filename":"j.neucom.2021.11.043.pdf","filesize":[{"value":"6.7 MB"}],"format":"application/pdf","mimetype":"application/pdf","url":{"url":"https://kyutech.repo.nii.ac.jp/record/2000321/files/j.neucom.2021.11.043.pdf"},"version_id":"54e14470-7c9f-43e6-8ce1-31c56ca8c074"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Multi-task unsupervised learning","subitem_subject_scheme":"Other"},{"subitem_subject":"Multi-level modeling","subitem_subject_scheme":"Other"},{"subitem_subject":"Small sample size problem","subitem_subject_scheme":"Other"},{"subitem_subject":"Manifold disentanglement","subitem_subject_scheme":"Other"},{"subitem_subject":"Meta-learning","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Multi-task manifold learning for small sample size datasets","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Multi-task manifold learning for small sample size datasets","subitem_title_language":"en"}]},"item_type_id":"21","owner":"14","path":["24"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2023-12-18"},"publish_date":"2023-12-18","publish_status":"0","recid":"2000321","relation_version_is_last":true,"title":["Multi-task manifold learning for small sample size datasets"],"weko_creator_id":"14","weko_shared_id":-1},"updated":"2025-07-14T02:26:20.912243+00:00"}