{"created":"2023-06-26T11:00:06.469227+00:00","id":315,"links":{},"metadata":{"_buckets":{"deposit":"c90144dc-5bdd-4cde-b8ad-acc2650ce69a"},"_deposit":{"created_by":27,"id":"315","owners":[27],"pid":{"revision_id":0,"type":"depid","value":"315"},"status":"published"},"_oai":{"id":"oai:oist.repo.nii.ac.jp:00000315","sets":["6:26"]},"author_link":["1091","1097","1096","1094","1098","1095","1092","1093"],"item_10001_biblio_info_7":{"attribute_name":"Bibliographic Information","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2017-10-19","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicPageStart":"e0186566","bibliographicVolumeNumber":"12","bibliographic_titles":[{},{"bibliographic_title":"PLOS ONE","bibliographic_titleLang":"en"}]}]},"item_10001_creator_3":{"attribute_name":"Author","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tokuda, Tomoki"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yoshimoto, Junichiro"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shimizu, Yu"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Okada, Go"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takamura, Masahiro"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Okamoto, Yasumasa"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yamawaki, Shigeto"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Doya, Kenji"}],"nameIdentifiers":[{}]}]},"item_10001_description_5":{"attribute_name":"Abstract","attribute_value_mlt":[{"subitem_description":"We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.","subitem_description_type":"Other"}]},"item_10001_publisher_8":{"attribute_name":"Publisher","attribute_value_mlt":[{"subitem_publisher":"PLOS"}]},"item_10001_relation_13":{"attribute_name":"PubMedNo.","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"info:pmid/29049392","subitem_relation_type_select":"PMID"}}]},"item_10001_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"info:doi/10.1371/journal.pone.0186566","subitem_relation_type_select":"DOI"}}]},"item_10001_relation_17":{"attribute_name":"Related site","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0186566","subitem_relation_type_select":"URI"}}]},"item_10001_rights_15":{"attribute_name":"Rights","attribute_value_mlt":[{"subitem_rights":"© 2017 Tokuda et al."}]},"item_10001_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1932-6203","subitem_source_identifier_type":"ISSN"}]},"item_10001_version_type_20":{"attribute_name":"Author's flag","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2018-03-12"}],"displaytype":"detail","filename":"journal.pone.0186566.pdf","filesize":[{"value":"39.7 MB"}],"format":"application/pdf","license_note":"Creative Commons Attribution 4.0 International  \n(http://creativecommons.org/licenses/by/4.0/)","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"Full-Text","url":"https://oist.repo.nii.ac.jp/record/315/files/journal.pone.0186566.pdf"},"version_id":"4ec8704e-b57b-405c-8304-4fb77c0e8b01"}]},"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":"Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions","subitem_title_language":"en"}]},"item_type_id":"10001","owner":"27","path":["26"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-03-12"},"publish_date":"2018-03-12","publish_status":"0","recid":"315","relation_version_is_last":true,"title":["Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions"],"weko_creator_id":"27","weko_shared_id":27},"updated":"2023-06-26T12:09:57.295158+00:00"}