Dynamic time warping constraint learning for large margin nearest neighbor classification |
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Authors: | Daren Yu Qinghua Hu Jinfu Liu Anqi Wu |
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Affiliation: | Harbin Institute of Technology, Harbin 150001, China |
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Abstract: | Nearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification. The performance of NN-DTW is dependent on the DTW constraints because the NN classifier is sensitive to the used distance function. For time series classification, the global path constraint of DTW is learned for optimization of the alignment of time series by maximizing the nearest neighbor hypothesis margin. In addition, a reduction technique is combined with a search process to condense the prototypes. The approach is implemented and tested on UCR datasets. Experimental results show the effectiveness of the proposed method. |
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Keywords: | Time series classification Dynamic time warping Constraint learning Large margin |
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