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ICML
2008
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A reproducing kernel Hilbert space framework for pairwise time series distances

10 years 3 months ago
A reproducing kernel Hilbert space framework for pairwise time series distances
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.
Zhengdong Lu, Todd K. Leen, Yonghong Huang, Deniz
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2008
Where ICML
Authors Zhengdong Lu, Todd K. Leen, Yonghong Huang, Deniz Erdogmus
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