Abstract
Estimating the centroid of a set of time series under time warp is a major topic for many temporal data mining applications, as summarization a set of time series, prototype extraction or clustering. The task is challenging as the estimation of centroid of time series faces the problem of multiple temporal alignments. This work compares the major progressive and iterative centroid estimation methods, under the dynamic time warping, which currently is the most relevant similarity measure in this context.
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Abdulla, W.H., Chow, D., Sin, G.: Cross-words reference template for DTW-based speech recognition systems. In: Proceedings of TENCON, vol. 2, pp. 1576–1579 (2003)
Hautamaki, V., Nykanen, P., Franti, P.: Time-series clustering by approximate prototypes. In: 19th International Conference on Pattern Recognition (2008)
Petitjean, F., Ketterlin, A., Gançarski, P.: A global averaging method for dynamic time warping, with applications to clustering. Pattern Recogn. 44, 678–693 (2011)
Zhou, F., De la Torre, F.: Generalized time warping for multi-modal alignment of human motion. In: Computer Vision and Pattern Recognition (CVPR), pp. 1282–1289. IEEE (2012)
Kruskall, J.B., Liberman, M.: The symmetric time warping algorithm: from continuous to discrete. Time Warps Journal. Addison-Wesley (1983)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech, Signal Process. 26, 43–49 (1978)
Sankoff, D., Kruskal, J.B.: Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison. Cambridge University Press, Addison-Wesley, Reading (1983)
Thompson, J.D., Higgins, D.G., Gibson, T.J.: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. J. Comput. Biol. 22(22), 4673–4680 (1994)
Notredame, C., Higgins, D.-G., Heringa, J.: T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. - J MOL BIOL 302(1), 205–217 (2000)
Sze, S.-H., Lu, Y., Yang, Q.: A polynomial time solvable formulation of multiple sequence alignment. J. Comput. Biol. 13(2), 309–319 (2006)
Carrillo, H., Lipman, D.: The multiple sequence alignment problem in biology. SIAM J. Appl. Math. 48, 1073–1082 (1988). Society for Industrial and Applied Mathematics, Philadelphia, PA, USA
Gupta, L., Molfese, D., Tammana, R., Simos, P.: Nonlinear alignment and averaging for estimating the evoked potential. IEEE Trans. Biomed. Eng. 43(4), 348–356 (1996)
Soheily-Khah, S., Douzal-Chouakria, A., Gaussier, E.: Progressive and iterative approaches for time series averaging. In: ECML-PKDD (Advanced Analytics and Learning on Temporal Data) (2015)
UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/
UCR Time Series Classification Archive. http://www.cs.ucr.edu/~eamonn/
Niennattrakul, N., Ratanamahatana, C.: On clustering multimedia time series data using K-means and dynamic time warping. In: International onference on IEEE Multimedia and Ubiquitous Engineering, MUE 2007, pp. 733–738 (2007)
Niennattrakul, N., Ratanamahatana, C.: Shape averaging under time warping. In: ECTI-CON 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. IEEE, vol. 2, pp. 626–629, May 2009
Zhou, F., De la Torre, F.: Canonical time warping for alignment of human behavior. Adv. Neural Inf. Process. Syst. 22, 2286–2294 (2009)
Frambourg, C., Douzal-Chouakria, A., Gaussier, E.: Learning multiple temporal matching for time series classification. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds.) IDA 2013. LNCS, vol. 8207, pp. 198–209. Springer, Heidelberg (2013)
Soheily-Khah, S., Douzal-Chouakria, A., Gaussier, E.: Generalized \(k\)-means-based clustering for temporal data under weighted and kernel time warp. J. Pattern Recogn. Lett. 75, 63–69 (2016). doi:10.1016/j.patrec.2016.03.007
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Soheily-Khah, S., Douzal-Chouakria, A., Gaussier, E. (2016). A Comparison of Progressive and Iterative Centroid Estimation Approaches Under Time Warp. In: Douzal-Chouakria, A., Vilar, J., Marteau, PF. (eds) Advanced Analysis and Learning on Temporal Data. AALTD 2015. Lecture Notes in Computer Science(), vol 9785. Springer, Cham. https://doi.org/10.1007/978-3-319-44412-3_10
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