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- W2158677029 abstract "A good distance measure for time series needs to properly incorporate the temporal structure, and should be applicable to sequences with unequal lengths. In this paper, we propose a distance measure as a principled solution to the two requirements. Unlike the conventional feature vector representation, our approach represents each time series with a summarizing smooth curve in a reproducing kernel Hilbert space (RKHS), and therefore translate the distance between time series into distances between curves. Moreover we propose to learn the kernel of this RKHS from a population of time series with discrete observations using Gaussian process-based non-parametric mixed-effect models. Experiments on two vastly different real-world problems show that the proposed distance measure leads to improved classification accuracy over the conventional distance measures." @default.
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- W2158677029 date "2008-01-01" @default.
- W2158677029 modified "2023-09-26" @default.
- W2158677029 title "A reproducing kernel Hilbert space framework for pairwise time series distances" @default.
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- W2158677029 doi "https://doi.org/10.1145/1390156.1390235" @default.
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