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| Curve Registration Method for Maximizing Correlation Coefficient Based on Non-uniform Sampling |
| ZHANG Wenkai1, WANG Wenjian1,2, JIANG Gaoxia1 |
1.School of Computer and Information Technology, Shanxi University, Taiyuan 030006
2.Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006 |
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Abstract In functional data analysis, two kinds of non-uniform sampling methods for curve registration are put forward to improve the efficiency. Slope based non-uniform sampling (SBNS) method samples according to the slope size of the function curve. Arc length based non-uniform sampling (ALBNS) method samples evenly in the arc length of function curve. Two non-uniform sampling methods sample according to characteristics of curves instead of sampling evenly in the time axis. Thus, the defects of uniform sampling method caused by the number and the location distribution of sample points are overcome and the effect of curve registration is improved. The experimental results on simulated data and real data show that the above two kinds of methods are better than uniform sampling method in time efficiency and the effect of curve registration.
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Received: 28 April 2015
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| About author:: (ZHANG Wenkai， born in 1990， master student. His research interests include analysis and application of functional data.)(Wang Wenjian (Corresponding author)， born in 1968，Ph.D.， professor. Her research interests include machine learning， computing intelligence， image processing， etc.)(Jiang Gaoxia， born in 1987， Ph.D. candidate. His research interests include functional data analysis and machine learning.) |
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[1] FU T C. A Review on Time Series Data Mining. Engineering Applications of Artificial Intelligence, 2010, 24(1): 164-181.
[2] RAKTHANMANON T, CAMPANA B, MUEEN A, et al. Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping // Proc of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Beijing, China, 2012: 262-270.
[3] JEONG Y S, JEONG M K, OMITAOMU O A. Weighted Dynamic Time Warping for Time Series Classification. Pattern Recognition, 2011, 44(9): 2231-2240.
[4] PETITJEAN F, KETTERLIN A, GANCARSKI P. A Global Ave-raging Method for Dynamic Time Warping with Applications to Clustering. Pattern Recognition, 2011, 44(3): 678-693.
[5] 林小雨,江 弋,赖永炫,等.一种新的时间序列延迟相关性分析算法——三点预测探查法.计算机研究与发展, 2012, 49(12): 2645-2655.
(Lin Z Y, Jiang Y, Lai Y X, et al. A New Algorithm on Lagged Correlation Analysis between Time Series: TPFP. Journal of Computer Research and Development, 2012, 49(12): 2645-2655.)
[6] KIM S W, PARK S, CHU W W. Efficient Processing of Similarity Search under Time Warping in Sequence Databases: An Index-Based Approach. Information Systems, 2004, 29(5): 405-420.
[7] RAMSAY J O. Matlab, R and S-PLUS Functions for Functional Data Analysis. New York, USA: Springer, 2003.
[8] RAMSAY J O, SILVERMAN B W. Functional Data Analysis. 2nd Edition. New York, USA: Springer, 2006.
[9] RAMSAY J O, HOOKER G, GRAVES S. Functional Data Analysis with R and MATLAB. New York, USA: Springer, 2009.
[10] RAMSAY J O, LI X. Curve Registration. Journal of the Royal Statistical Society (Statistical Methodology), 1998, 60(2): 351-363.
[11] 姜高霞,王文剑.时序数据曲线排齐的相关性分析方法.软件学报, 2014, 25(9): 2002-2017.
(JIANG G X, WANG W J. Correlation Analysis in Curve Registration of Time Series. Journal of Software, 2014, 25(9): 2002-2017.)
[12] YU D R, YU X, HU Q H, et al. Dynamic Time Warping Constraint Learning for Large Margin Nearest Neighbor Classification. Information Sciences, 2011, 181(13): 2787-2796.
[13] ARRIBAS-GIL A, MLLER H G. Pairwise Dynamic Time Warping for Event Data. Computational Statistics & Data Analysis, 2014, 69: 255-268.
[14] YING X, LI F P. Feature Based Dynamic Time Warping // Proc of the 2nd International Conference on Advanced Computer Theory and Engineering. Cairo, Egypt, 2009: 1785-1792. |
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2016, Vol. 29 
