{"created":"2023-05-15T12:10:06.582719+00:00","id":15757,"links":{},"metadata":{"_buckets":{"deposit":"97a30047-d505-41b3-b131-4ed9b2a13ae3"},"_deposit":{"created_by":19,"id":"15757","owners":[19],"pid":{"revision_id":0,"type":"depid","value":"15757"},"status":"published"},"_oai":{"id":"oai:iwate-u.repo.nii.ac.jp:00015757","sets":["1515:1519"]},"author_link":["94910"],"item_16_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2021-04-09","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"56577","bibliographicPageStart":"56563","bibliographicVolumeNumber":"9","bibliographic_titles":[{"bibliographic_title":"IEEE Access"}]}]},"item_16_date_6":{"attribute_name":"登録日","attribute_value_mlt":[{"subitem_date_issued_datetime":"2022-08-04"}]},"item_16_description_11":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"In machine learning, observation features are measured in a metric space to obtain their distance function for optimization. Given similar features that are statistically sufficient as a population, a statistical distance between two probability distributions can be calculated for more precise learning. Provided the observed features are multi-valued, the statistical distance function is still efficient. However, due to its scalar output, it cannot be applied to represent detailed distances between feature elements. To resolve this problem, this paper extends the traditional statistical distance to a matrix form, called a statistical distance matrix. The proposed approach performs well in object recognition tasks and clearly and intuitively represents the dissimilarities between cat and dog images in the CIFAR dataset, even when directly calculated using the image pixels. By using the hierarchical clustering of the statistical distance matrix, the image pixels can be separated into several clusters that are geometrically arranged around a center like a Mandala pattern. The statistical distance matrix with clustering is called the Information Mandala.","subitem_description_type":"Abstract"}]},"item_16_link_13":{"attribute_name":"抄録（URL）","attribute_value_mlt":[{"subitem_link_text":"Information Mandala : Statistical Distance Matrix With Clustering","subitem_link_url":"https://ieeexplore.ieee.org/document/9399434"}]},"item_16_publisher_14":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"IEEE"}]},"item_16_relation_26":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"info:doi/10.1109/ACCESS.2021.3072237","subitem_relation_type_select":"DOI"}}]},"item_16_rights_18":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"@2021 by the author"}]},"item_16_rights_19":{"attribute_name":"権利URI","attribute_value_mlt":[{"subitem_rights":"https://creativecommons.org/licenses/by-nc-nd/4.0/"}]},"item_16_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"21693536","subitem_source_identifier_type":"ISSN"}]},"item_16_text_4":{"attribute_name":"著者(機関)","attribute_value_mlt":[{"subitem_text_value":"Faculty of Science and Engineering, Iwate University"}]},"item_16_version_type_27":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"LU, XIN"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2022-08-04"}],"displaytype":"detail","filename":"ieeea-v9p56563-56577.pdf","filesize":[{"value":"6.8 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"ieeea-v9p56563-56577","url":"https://iwate-u.repo.nii.ac.jp/record/15757/files/ieeea-v9p56563-56577.pdf"},"version_id":"7270ea28-e6d3-44ae-9d3d-066581df0135"}]},"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":"Information Mandala : Statistical Distance Matrix with Clustering","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Information Mandala : Statistical Distance Matrix with Clustering"}]},"item_type_id":"16","owner":"19","path":["1519"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-08-04"},"publish_date":"2022-08-04","publish_status":"0","recid":"15757","relation_version_is_last":true,"title":["Information Mandala : Statistical Distance Matrix with Clustering"],"weko_creator_id":"19","weko_shared_id":-1},"updated":"2023-05-15T12:52:23.658394+00:00"}