TY - GEN
T1 - A reproducing kernel Hilbert space framework for pairwise time series distances
AU - Lu, Zhengdong
AU - Leen, Todd K.
AU - Huang, Yonghong
AU - Erdogmus, Deniz
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=56449091021&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=56449091021&partnerID=8YFLogxK
U2 - 10.1145/1390156.1390235
DO - 10.1145/1390156.1390235
M3 - Conference contribution
AN - SCOPUS:56449091021
SN - 9781605582054
T3 - Proceedings of the 25th International Conference on Machine Learning
SP - 624
EP - 631
BT - Proceedings of the 25th International Conference on Machine Learning
PB - Association for Computing Machinery (ACM)
T2 - 25th International Conference on Machine Learning
Y2 - 5 July 2008 through 9 July 2008
ER -