Sparse nonnegative dynamic mode decomposition

Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781509021758
Publication statusPublished - Feb 20 2018
Externally publishedYes
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: Sept 17 2017Sept 20 2017

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference24th IEEE International Conference on Image Processing, ICIP 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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