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A global averaging method for dynamic time warping, with applications to clustering

A global averaging method for dynamic time warping, with applications to clustering. Presenter : Jiang-Shan Wang Authors : Francois Petitjean, Alain Ketterlin, Pierre Gancarski. 國立雲林科技大學 National Yunlin University of Science and Technology. PR 2011. Outline. Motivation Objective

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A global averaging method for dynamic time warping, with applications to clustering

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  1. A global averaging method for dynamic time warping, with applications to clustering Presenter : Jiang-Shan Wang Authors : Francois Petitjean, Alain Ketterlin, Pierre Gancarski 國立雲林科技大學 National Yunlin University of Science and Technology PR 2011

  2. Outline • Motivation • Objective • Method • Experiment • Conclusion • Comments

  3. Motivation • To improve the drawbacks of previous studies. • Pairwise averaging => sensitive to the order. • Local averaging => initial approximation error propagate. • To avoid long and detailed average sequences. • Because the complexity of Dynamic Time Warping (DTW) is directly related to the length of the sequences.

  4. Objective To propose a global averaging method for dynamic time warping.

  5. Method DTW barycenter averaging(DBA)

  6. Experiment Datasets

  7. Experiment

  8. Experiment Different initialization

  9. Experiment Adaptive scaling(AS)

  10. Experiment Satellite image time series

  11. Conclusion DBA achieves better results on all tested datasets and its behavior is robust. Adaptive scaling is shown to shorten the average sequence in adequacy to DTW and to the data, but also to improve its representativity.

  12. Comments • Advantage • Many experiments • Reducing computational complexity • Drawback • Some mistake • Application • Sequence data clustering

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