Time Series Classification by Class-Based Mahalanobis Distances
نویسندگان
چکیده
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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ورودعنوان ژورنال:
- CoRR
دوره abs/1010.1526 شماره
صفحات -
تاریخ انتشار 2010