A global averaging method for dynamic time warping, with applications to clustering
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Transcript of A global averaging method for dynamic time warping, with applications to clustering
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
A global averaging method for dynamic time warping, with applications to clustering
Presenter : Jiang-Shan Wang
Authors : Francois Petitjean, Alain Ketterlin, Pierre Gancarski
PR 2011
國立雲林科技大學National Yunlin University of Science and Technology
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Outline
Motivation
Objective
Method
Experiment
Conclusion
Comments
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.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.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Objective
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To propose a global averaging method for dynamic time warping.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Method
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DTW barycenter averaging(DBA)
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiment
Datasets
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiment
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiment
Different initialization
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiment
Adaptive scaling(AS)
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiment
Satellite image time series
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Conclusion
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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.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Comments
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Advantage Many experiments
Reducing computational complexity
Drawback Some mistake
Application Sequence data clustering