{"created":"2023-05-15T15:01:27.518492+00:00","id":83373,"links":{},"metadata":{"_buckets":{"deposit":"fd41bb08-e939-45eb-baba-365fb9012cd0"},"_deposit":{"created_by":1,"id":"83373","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"83373"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00083373","sets":["10:29"]},"author_link":["1002298","1002300","1002299","1002301","1002302"],"item_10005_date_7":{"attribute_name":"発表年月日","attribute_value_mlt":[{"subitem_date_issued_datetime":"2021-09-21","subitem_date_issued_type":"Issued"}]},"item_10005_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Principal component analysis (PCA) is a widely used statistical tool for extracting low-dimensional structures underlying multivariate data. However, its application to high-dimensional data is limited due to its large computational time. While the conventional PCA algorithm requires polynomial time, using a quantum-inspired algorithm as a subroutine, we have implemented an algorithm that approximates it with computational time proportional to the logarithm of the input dimensionality. The computational efficiency and performance of the implemented algorithm, quantum-inspired PCA, are experimentally evaluated on synthetic and real datasets.","subitem_description_type":"Abstract"}]},"item_10005_description_6":{"attribute_name":"会議概要（会議名, 開催地, 会期, 主催者等）","attribute_value_mlt":[{"subitem_description":"第31回 日本神経回路学会 全国大会（JNNS2021）","subitem_description_type":"Other"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"間島, 慶"}],"nameIdentifiers":[{"nameIdentifier":"1002298","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"小出(間島)真子"}],"nameIdentifiers":[{"nameIdentifier":"1002299","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"八幡, 憲明"}],"nameIdentifiers":[{"nameIdentifier":"1002300","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Kei, Majima","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"1002301","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Noriaki, Yahata","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"1002302","nameIdentifierScheme":"WEKO"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2022-03-28"}],"displaytype":"detail","filename":"2e815879095ac94b2322e1ab9de7a57d.pdf","filesize":[{"value":"291.7 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"超高次元データ解析のための量子インスパイア主成分分析の開発","url":"https://repo.qst.go.jp/record/83373/files/2e815879095ac94b2322e1ab9de7a57d.pdf"},"version_id":"451f10d4-a1c5-4508-80ed-6a13f95df12d"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"conference object","resourceuri":"http://purl.org/coar/resource_type/c_c94f"}]},"item_title":"超高次元データ解析のための量子インスパイア主成分分析の開発","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"超高次元データ解析のための量子インスパイア主成分分析の開発"},{"subitem_title":"Quantum-inspired principal component analysis for exponentially large dimensional data","subitem_title_language":"en"}]},"item_type_id":"10005","owner":"1","path":["29"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-07-18"},"publish_date":"2021-07-18","publish_status":"0","recid":"83373","relation_version_is_last":true,"title":["超高次元データ解析のための量子インスパイア主成分分析の開発"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-15T19:34:35.262607+00:00"}