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.
https://hal.archives-ouvertes.fr/hal-01202045
Contributeur : Michele Rombaut
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Soumis le : mercredi 30 septembre 2015 - 11:58:24
Dernière modification le : mardi 24 juillet 2018 - 16:01:46
Document(s) archivé(s) le : jeudi 31 décembre 2015 - 10:12:03
Cao-Tri Do, Ahlame Douzal-Chouakria, Sylvain Marié, Michèle Rombaut. Multiple Metric Learning for large margin kNN Classification of time series. 23rd European Signal Processing Conference (EUSIPCO-2015), Sep 2015, Nice, France. 〈hal-01202045〉
