Learning with Coherence Patterns in Multivariate Time-series Data via Dynamic Mode Decomposition

Takehito Bito, Masashi Hiraoka, Yoshinobu Kawahara

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Understanding complex dynamics in the real world is a fundamental problem in various engineering and scientific fields. Dynamic mode decomposition (DMD) has attracted attention recently as a prominent way to obtain global modal descriptions of nonlinear dynamical processes from data without requiring explicit prior knowledge about the underlying systems. In this paper, we propose a novel learning method for multivariate time-series data involving complex dynamics using coherence patterns among attributes extracted by DMD. To this end, we develop kernels defined with Grassmann subspaces spanned by dynamic modes which are calculated by DMD and represent coherence patters among attributes with respect to the estimated modal dynamics. To incorporate information in labels attached to a set of time-series sequences, we employ a supervised embedding step in the DMD procedure. We illustrate and investigate the empirical performance of the proposed method using real-world data.

本文言語英語
ホスト出版物のタイトル2019 International Joint Conference on Neural Networks, IJCNN 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728119854
DOI
出版ステータス出版済み - 7 2019
イベント2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, ハンガリー
継続期間: 7 14 20197 19 2019

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks
2019-July

会議

会議2019 International Joint Conference on Neural Networks, IJCNN 2019
Countryハンガリー
CityBudapest
Period7/14/197/19/19

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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