{"created":"2021-03-01T07:19:29.389559+00:00","id":40142,"links":{},"metadata":{"_buckets":{"deposit":"10deb705-f0ca-44b5-8374-dfbe0892e365"},"_deposit":{"id":"40142","owners":[],"pid":{"revision_id":0,"type":"depid","value":"40142"},"status":"published"},"_oai":{"id":"oai:tsukuba.repo.nii.ac.jp:00040142","sets":["117:1697","117:786","3:62:5587:5638"]},"item_5_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2016","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicPageEnd":"917","bibliographicPageStart":"895","bibliographicVolumeNumber":"10","bibliographic_titles":[{"bibliographic_title":"Electronic Journal of Statistics"}]}]},"item_5_creator_3":{"attribute_name":"著者別名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"矢田, 和善"}],"nameIdentifiers":[{},{},{}]},{"creatorNames":[{"creatorName":"青嶋, 誠"}],"nameIdentifiers":[{},{},{}]}]},"item_5_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"We consider the problem of recovering a low-rank signal matrix in high-dimensional situations. The main issue is how to estimate the signal matrix in the presence of huge noise. We introduce the power spiked model to describe the structure of singular values of a huge data matrix. We first consider the conventional PCA to recover the signal matrix and show that the estimation of the signal matrix holds consistency properties under severe conditions. The conventional PCA is heavily subjected to the noise. In order to reduce the noise we apply the noise-reduction (NR) methodology and propose a new estimation of the signal matrix. We show that the proposed estimation by the NR method holds the consistency properties under mild conditions and improves the error rate of the conventional PCA effectively. Finally, we demonstrate the reconstruction procedures by using a microarray data set. ","subitem_description_type":"Abstract"}]},"item_5_publisher_27":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Institute of Mathematical Statistics"},{"subitem_publisher":"Bernoulli Society"}]},"item_5_relation_11":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1214/16-EJS1128","subitem_relation_type_select":"DOI"}}]},"item_5_select_15":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_select_item":"publisher"}]},"item_5_source_id_7":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1935-7524","subitem_source_identifier_type":"ISSN"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yata, Kazuyoshi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Aoshima, Makoto"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2017-01-26"}],"displaytype":"detail","filename":"EJS_10-1.pdf","filesize":[{"value":"414.8 kB"}],"format":"application/pdf","licensetype":"license_6","mimetype":"application/pdf","url":{"label":"EJS_10-1","url":"https://tsukuba.repo.nii.ac.jp/record/40142/files/EJS_10-1.pdf"},"version_id":"5ae4ac01-80e4-4923-9e81-74b69cdad94e"}]},"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":"Reconstruction of a high-dimensional low-rank matrix","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Reconstruction of a high-dimensional low-rank matrix"}]},"item_type_id":"5","owner":"1","path":["786","1697","5638"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-01-26"},"publish_date":"2017-01-26","publish_status":"0","recid":"40142","relation_version_is_last":true,"title":["Reconstruction of a high-dimensional low-rank matrix"],"weko_creator_id":"1","weko_shared_id":5},"updated":"2022-04-27T09:10:27.573886+00:00"}