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Conference papers

Multiple Metric Learning for large margin kNN Classification of time series

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.
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Contributor : Michèle Rombaut Connect in order to contact the contributor
Submitted on : Wednesday, September 30, 2015 - 11:58:24 AM
Last modification on : Monday, January 24, 2022 - 5:06:02 PM
Long-term archiving on: : Thursday, December 31, 2015 - 10:12:03 AM


Do et al. - 2015 - Multiple Me...
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  • HAL Id : hal-01202045, version 1



Cao-Tri Do, Ahlame Douzal-Chouakria, Sylvain Marié, Michèle Rombaut. Multiple Metric Learning for large margin kNN Classification of time series. EUSIPCO 2015 - 23th European Signal Processing Conference, Aug 2015, Nice, France. ⟨hal-01202045⟩



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