Multiple metric learning for large margin kNN classification of time series

Cao-Tri Do (), Ahlame Douzal-Chouakria, Sylvain Marie and Michele Rombaut
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Cao-Tri Do: GIPSA-lab - Grenoble Images Parole Signal Automatique - UPMF - Université Pierre Mendès France - Grenoble 2 - Université Stendhal - Grenoble 3 - UJF - Université Joseph Fourier - Grenoble 1 - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - CNRS - Centre National de la Recherche Scientifique
Ahlame Douzal-Chouakria: LIG - Laboratoire d'Informatique de Grenoble - UPMF - Université Pierre Mendès France - Grenoble 2 - UJF - Université Joseph Fourier - Grenoble 1 - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - INPG - Institut National Polytechnique de Grenoble - CNRS - Centre National de la Recherche Scientifique
Sylvain Marie: Schneider Electric (France) (France, Rueil-Malmaison)
Michele Rombaut: GIPSA-lab - Grenoble Images Parole Signal Automatique - UPMF - Université Pierre Mendès France - Grenoble 2 - Université Stendhal - Grenoble 3 - UJF - Université Joseph Fourier - Grenoble 1 - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - CNRS - Centre National de la Recherche Scientifique

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Abstract: Time series are complex data objects, they may present noise, varying delays or involve several temporal granularities. To classify time series, promising solutions refer to the combination of multiple basic metrics to compare time series according to several characteristics. This work proposes a new framework to learn a combination of multiple metrics for a robust kNN classifier. By introducing the concept of pairwise space, the combination function is learned in this new space through a "large margin" optimization process. We apply it to compare time series on both their values and behaviors. The efficiency of the learned metric is compared to the major alternative metrics on large public datasets.

Keywords: Multiple metric learning; Time series; kNN; Classification (search for similar items in EconPapers)
Date: 2015-08-31
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Published in European Signal Processing Conference (EUSIPCO) 23rd, Aug 2015, Nice, France. ⟨10.1109/EUSIPCO.2015.7362804⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05748340

DOI: 10.1109/EUSIPCO.2015.7362804

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