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Time Series Classification by Class-Based Mahalanobis Distances

7 October 2010
Zoltán Prekopcsák
D. Lemire
    AI4TS
ArXiv (abs)PDFHTML
Abstract

To classify time series by nearest neighbor, we need to specify or learn a distance. We consider several variations of the Mahalanobis distance and the related Large Margin Nearest Neighbor Classification (LMNN). We find that the conventional Mahalanobis distance is counterproductive. However, both LMNN and the class-based diagonal Mahalanobis distance are competitive.

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