Learning DTW-Preserving Shapelets

Arnaud Lods 1 Simon Malinowski 1 Romain Tavenard 2, 3, 4 Laurent Amsaleg 1
1 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA_D6 - MEDIA ET INTERACTIONS
2 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
4 LETG - Rennes - Littoral, Environnement, Télédétection, Géomatique
LETG - Littoral, Environnement, Télédétection, Géomatique
Abstract : Dynamic Time Warping (DTW) is one of the best similarity measures for time series, and it has extensively been used in retrieval, classification or mining applications. It is a costly measure, and applying it to numerous and/or very long times series is difficult in practice. Recently, Shapelet Transform (ST) proved to enable accurate supervised classification of time series. ST learns small subsequences that well discriminate classes, and transforms the time series into vectors lying in a metric space. In this paper, we adopt the ST framework in a novel way: we focus on learning, without class label information, shapelets such that Euclidean distances in the ST-space approximate well the true DTW. Our approach leads to an ubiquitous representation of time series in a metric space, where any machine learning method (supervised or unsupervised) and indexing system can operate efficiently.2017-
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International Symposium on Intelligent Data Analysis, Oct 2017, London, United Kingdom. springer International Publishing, Sixteenth International Symposium on Intelligent Data Analysis (IDA 2017) 2017, International Symposium on Intelligent Data Analysis
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Contributeur : Laurent Amsaleg <>
Soumis le : mercredi 19 juillet 2017 - 15:52:11
Dernière modification le : samedi 23 septembre 2017 - 01:07:58

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Arnaud Lods, Simon Malinowski, Romain Tavenard, Laurent Amsaleg. Learning DTW-Preserving Shapelets. International Symposium on Intelligent Data Analysis, Oct 2017, London, United Kingdom. springer International Publishing, Sixteenth International Symposium on Intelligent Data Analysis (IDA 2017) 2017, International Symposium on Intelligent Data Analysis. <hal-01565207>

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