{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00194860","sets":["1164:4088:9695:9718"]},"path":["9718"],"owner":"44499","recid":"194860","title":["K-SVD法を用いたネットワークトポロジのスパース表現化に関する検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-02-28"},"_buckets":{"deposit":"4a884b01-5834-439f-9d07-da371e791c5f"},"_deposit":{"id":"194860","pid":{"type":"depid","value":"194860","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"K-SVD法を用いたネットワークトポロジのスパース表現化に関する検討","author_link":["462400","462407","462402","462406","462405","462401","462403","462404"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"K-SVD法を用いたネットワークトポロジのスパース表現化に関する検討"},{"subitem_title":"A Study on Sparse Representation of Network Topology with K-SVD Algorithm","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"グラフ/学習","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2019-02-28","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"関西学院大学理工学部情報科学科"},{"subitem_text_value":"関西学院大学大学院理工学研究科情報科学専攻"},{"subitem_text_value":"関西学院大学大学院理工学研究科情報科学専攻"},{"subitem_text_value":"関西学院大学大学院理工学研究科情報科学専攻"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Informatics, School of Science and Technology, Kwansei Gakuin University","subitem_text_language":"en"},{"subitem_text_value":"Department of Informatics, Graduate School of Science and Technology, Kwansei Gakuin University","subitem_text_language":"en"},{"subitem_text_value":"Department of Informatics, Graduate School of Science and Technology, Kwansei Gakuin University","subitem_text_language":"en"},{"subitem_text_value":"Department of Informatics, Graduate School of Science and Technology, Kwansei Gakuin University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/194860/files/IPSJ-IOT19044051.pdf","label":"IPSJ-IOT19044051.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-IOT19044051.pdf","filesize":[{"value":"330.6 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"43"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"7b4284d8-0b1d-47e0-a5d8-911d08a9bbe3","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"村上, 龍"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松尾, 涼太郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中村, 遼"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大崎, 博之"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Ryu, Murakami","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryotaro, Matsuo","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryo, Nakamura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroyuki, Ohsaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12326962","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8787","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年，スパースモデリングと呼ばれる，モデルの特徴量が有するスパース性を利用することで，少数の観測値からモデルの未知の特徴量を推定する統計的手法が注目を浴びている．信号処理や画像処理の分野を中心に研究が進んでいるスパースモデリングであるが，情報ネットワーク分野における応用の検討も始まっている．本稿では，スパースモデリングにおける辞書学習アルゴリズムによって構築した辞書を用いることにより，ネットワークトポロジのスパース表現化がどの程度可能かを調査する．具体的には，スパースモデリングにおける代表的な辞書学習アルゴリズム K-SVD 法を用いて，多数の学習用ネットワークトポロジから辞書を構築し，入力となるネットワークトポロジと構築した辞書に対して lo ノルム最小化問題を解くことにより，ネットワークトポロジのスパース表現を求める．さらに実験により，ネットワークトポロジの構造や規模，辞書の規模が，ネットワークトポロジのスパース表現化にどのような影響を与えるかを調査する．その結果，ツリーのような規則的な構造を持つグラフや，クラスター構造を有するようなネットワークがネットワークトポロジのスパース表現化に適していることなどがわかった．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In recent years, a statistical approach called sparse modeling has been studied extensively for estimating unobserved model parameters from a small number of observations by using the sparsity of model parameters. Although sparse modeling has been applied to many practical problems in the fields of signal processing and image processing, to the best of our knowledge, few studies have applied it to the field of information networking. In this paper, we investigate whether sparse representation of network topology can be obtained from a dictionary trained with a dictionary teaming algorithm in sparse modeling. Specifically, we train a dictionary from a number of learning network topologies by using K-SVD algorithm, which is one of conventional dictionary learning algorithms, and obtain sparse representation of the network topology by solving an lo-norm minimization problem for a given network topology and the trained dictionary. Furthermore, through experiments, effects of several factors — the network (i.e., topology and network size) and the dictionary (i.e., dictionary size) — on sparse representation of network topologies are investigated. Our finding includes that graphs whose stmcture is uniform (e.g., tree) and networks with cluster stmcture are suitable for sparse represenation of network topologies.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告インターネットと運用技術（IOT）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2019-02-28","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"51","bibliographicVolumeNumber":"2019-IOT-44"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T23:18:36.907316+00:00","created":"2025-01-19T00:59:53.234881+00:00","id":194860}