Abstract
We propose an approach to embed time series data in a vector space based on the distances obtained from Dynamic Time Warping (DTW), and to classify them in the embedded space. Under the problem setting in which both labeled data and unlabeled data are given beforehand, we consider three embeddings, embedding in a Euclidean space by MDS, embedding in a Pseudo-Euclidean space, and embedding in a Euclidean space by the Laplacian eigenmap technique.
We have found through analysis and experiment that the embedding by the Laplacian eigenmap method leads to the best classification result. Furthermore, the proposed approach with Laplacian eigenmap embedding shows better performance than k-nearest neighbor method.
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Hayashi, A., Mizuhara, Y., Suematsu, N. (2005). Embedding Time Series Data for Classification. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_35
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DOI: https://doi.org/10.1007/11510888_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26923-6
Online ISBN: 978-3-540-31891-0
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