A reproducing kernel Hilbert space framework for pairwise time series distances

Zhengdong Lu, Todd K. Leen, Yonghong Huang, Deniz Erdogmus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 25th International Conference on Machine Learning
Pages624-631
Number of pages8
StatePublished - Nov 26 2008
Event25th International Conference on Machine Learning - Helsinki, Finland
Duration: Jul 5 2008Jul 9 2008

Publication series

NameProceedings of the 25th International Conference on Machine Learning

Other

Other25th International Conference on Machine Learning
CountryFinland
CityHelsinki
Period7/5/087/9/08

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Software

Cite this

Lu, Z., Leen, T. K., Huang, Y., & Erdogmus, D. (2008). A reproducing kernel Hilbert space framework for pairwise time series distances. In Proceedings of the 25th International Conference on Machine Learning (pp. 624-631). (Proceedings of the 25th International Conference on Machine Learning).