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次元削減技術を用いた視覚的テンソルデータ解析
Kobe University, Japan.
Kobe University, Japan.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
RIKEN R-CCS, Japan.
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2022 (Japanese)Report (Other academic)Alternative title
Visual Analytics of Tensor Data with Dimensionality Reduction Techniques (English)
Abstract [ja]

多次元時系列データから,そこに内在する特徴構造を抽出し解釈するためのデータ解析手法に対する要求が高まっている.本研究では、解析対象とするデータを時間・空間・変数を軸(モード)とするテンソルデータとして表現し,多段階次元削減技術を応用することで,特徴構造を効果的に視覚化し,対話的にデータ探索を行うことができる視覚的解析手法を開発する.開発した手法を,実世界上で計測された時系列データ(スパコンログデータなど)に適用し,その有効性を検証する.

Place, publisher, year, edition, pages
2022. p. 1-1
Keywords [en]
time series, dimensionality reduction, big data
Keywords [ja]
時系列,次元削減,ビッグデータ
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-187999OAI: oai:DiVA.org:liu-187999DiVA, id: diva2:1692311
Available from: 2022-09-01 Created: 2022-09-01 Last updated: 2022-09-07Bibliographically approved

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Fujiwara, Takanori

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