Asymmetric Learning Vector Quantization for Efficient Nearest Neighbor Classification in Dynamic Time Warping Spaces

The nearest neighbor (NN) classifier endowed with the dynamic time warping (DTW) distance is one of the most popular methods in time series classification [9, 44]. Application examples include electrocardiogram frame classification [16], gesture recognition [2, 32], speech recognition [24], and voice recognition [23]. Two disadvantages of the naive NN method are high storage and computation requirements. Storage requirements are high, because the entire training set needs to be retained for being able to execute its classification rule. Computation requirements are high, because classifying a test example demands calculation of DTW distances between the test and all training examples.