Sparse nonnegative dynamic mode decomposition

Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi

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

4 引用 (Scopus)

抜粋

Dynamic mode decomposition (DMD) is a method to extract coherent modes from nonlinear dynamical systems. In this paper, we propose an extension of DMD, sparse nonnegative DMD, which generates a nonlinear and sparse modal representation of dynamics. In particular, this makes DMD more suitable for video processing. We reformulate DMD as a block-multiconvex optimization problem to impose constraints and regularizations directly on the structures of the estimated dynamic modes. We introduce the results of experiments with synthetic data and a surveillance video dataset and show that sparse nonnegative DMD can extract part-based dynamic modes from video streams.

元の言語英語
ホスト出版物のタイトル2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
出版者IEEE Computer Society
ページ2682-2686
ページ数5
ISBN(電子版)9781509021758
DOI
出版物ステータス出版済み - 2 20 2018
外部発表Yes
イベント24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, 中国
継続期間: 9 17 20179 20 2017

出版物シリーズ

名前Proceedings - International Conference on Image Processing, ICIP
2017-September
ISSN(印刷物)1522-4880

会議

会議24th IEEE International Conference on Image Processing, ICIP 2017
中国
Beijing
期間9/17/179/20/17

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • これを引用

    Takeishi, N., Kawahara, Y., & Yairi, T. (2018). Sparse nonnegative dynamic mode decomposition. : 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings (pp. 2682-2686). (Proceedings - International Conference on Image Processing, ICIP; 巻数 2017-September). IEEE Computer Society. https://doi.org/10.1109/ICIP.2017.8296769