Learning a Mahalanobis Distance-Based Dynamic Time Warping Measure for Multivariate Time Series Classification

IEEE Transactions on Cybernetics

Volumn 46, Issue 6, 2016, Pages 1363-1374

  Mei, Jiangyuan ^a,b   Liu, Meizhu ^c   Wang, Yuan Fang ^b   Gao, Huijun ^d  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b University of California   (United States)

^c YAHOO RESEARCH   (United States)

^d KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

Dynamic time warping (DTW); Mahalanobis distance; metric learning; multivariate time series (MTS)

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOMETRICS; LEARNING SYSTEMS; PATTERN RECOGNITION; TIME SERIES;

CONSTRAINT MODEL; DYNAMIC TIME WARPING; HOT RESEARCH TOPICS; MACHINE LEARNING REPOSITORY; MAHALANOBIS DISTANCES; MULTIVARIATE TIME SERIES; MULTIVARIATE TIME SERIES CLASSIFICATIONS; UNIVERSITY OF CALIFORNIA;

COMPUTER VISION;

EID: 84929017951     PISSN: 21682267     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCYB.2015.2426723     Document Type: Article

References (42)

1. T. W. Liao, "Clustering of time series data-A survey, " Pattern Recognit., vol. 38, no. 11, pp. 1857-1874, 2005.
2. E. Ramasso, M. Rombaut, and N. Zerhouni, "Joint prediction of continuous and discrete states in time-series based on belief functions, " IEEE Trans. Cybern., vol. 43, no. 1, pp. 37-50, Feb. 2013.
3. F. Rasheed and R. Alhajj, "A framework for periodic outlier pattern detection in time-series sequences, " IEEE Trans. Cybern., vol. 44, no. 5, pp. 569-582, May 2014.
4. C. Gruber, T. Gruber, S. Krinninger, and B. Sick, "Online signature verification with support vector machines based on LCSS kernel functions, " IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 40, no. 4, pp. 1088-1100, Aug. 2010.
5. W.-M. Li, F. Li, J.-W. Liu, and J.-J. Le, "Similarity search over data stream using LPC-DTW, " in Proc. Int. Conf. Mach. Learn. Cybern., vol. 3. Hong Kong, 2007, pp. 1631-1634.
6. M. Lewandowski, D. Makris, S. A. Velastin, and J.-C. Nebel, "Structural Laplacian eigenmaps for modeling sets of multivariate sequences, " IEEE Trans. Cybern., vol. 44, no. 6, pp. 936-949, Jun. 2013.
7. Z. Prekopcsák and D. Lemire, "Time series classification by classspecific Mahalanobis distance measures, " Adv. Data Anal. Classif., vol. 6, no. 3, pp. 185-200, 2012.
8. T. Górecki and M. Luczak, "Using derivatives in time series classification, " Data Min. Knowl. Disc., vol. 26, no. 2, pp. 310-331, 2013.
9. Y.-S. Jeong, M. K. Jeong, and O. A. Omitaomu, "Weighted dynamic time warping for time series classification, " Pattern Recognit., vol. 44, no. 9, pp. 2231-2240, 2011.
10. E. S. García-Treviño and J. A. Barria, "Structural generative descriptions for time series classification, " IEEE Trans. Cybern., vol. 44, no. 10, pp. 1978-1991, Oct. 2014.
11. X. Weng and J. Shen, "Classification of multivariate time series using two-dimensional singular value decomposition, " Knowl.-Based Syst., vol. 21, no. 7, pp. 535-539, 2008.
12. K. Yang and C. Shahabi, "A PCA-based similarity measure for multivariate time series, " in Proc. 2nd ACM Int. Workshop Multimedia Databases, Washington, DC, USA, 2004, pp. 65-74.
13. C. S. Möller-Levet, F. Klawonn, K.-H. Cho, and O. Wolkenhauer, "Fuzzy clustering of short time-series and unevenly distributed sampling points, " in Advances in Intelligent Data Analysis V. Berlin, Germany: Springer, 2003, pp. 330-340.
14. M. Kumar, N. R. Patel, and J. Woo, "Clustering seasonality patterns in the presence of errors, " in Proc. 8th ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., Edmonton, AB, Canada, 2002, pp. 557-563.
15. R. Dahlhaus, "On the Kullback-Leibler information divergence of locally stationary processes, " Stochast. Process. Appl., vol. 62, no. 1, pp. 139-168, 1996.
16. X. Weng and J. Shen, "Classification of multivariate time series using locality preserving projections, " Knowl.-Based Syst., vol. 21, no. 7, pp. 581-587, 2008.
17. D. J. Berndt and J. Clifford, "Using dynamic time warping to find patterns in time series, " in Proc. KDD Workshop, Seattle, WA, USA, 1994, pp. 359-370.
18. C. Orsenigo and C. Vercellis, "Combining discrete SVM and fixed cardinality warping distances for multivariate time series classification, " Pattern Recognit., vol. 43, no. 11, pp. 3787-3794, 2010.
19. H.-S. Lim, K.-Y. Whang, and Y.-S. Moon, "Similar sequence matching supporting variable-length and variable-Tolerance continuous queries on time-series data stream, " Inf. Sci., vol. 178, no. 6, pp. 1461-1478, 2008.
20. S. Salvador and P. Chan, "Toward accurate dynamic time warping in linear time and space, " Intell. Data Anal., vol. 11, no. 5, pp. 561-580, 2007.
21. G. T. Holt, M. Reinders, and E. Hendriks, "Multi-dimensional dynamic time warping for gesture recognition, " in Proc. 13th Annu. Conf. Adv. School Comput. Imag., vol. 300. Heijen, The Netherlands, 2007, pp. 1-8.
22. J. Mei, M. Liu, H. R. Karimi, and H. Gao, "LogDet divergence based metric learning using triplet labels, " in Proc. ICML Workshop Diverg. Diverg. Learn., Atlanta, GA, USA, 2013, pp. 1-9.
23. K. Q. Weinberger, J. Blitzer, and L. K. Saul, "Distance metric learning for large margin nearest neighbor classification, " in Proc. Adv. Neural Inf. Process. Syst., Vancouver, BC, USA, 2006, pp. 1-8.
24. C. Shen, J. Kim, L. Wang, and A. van den Hengel, "Positive semidefinite metric learning with boosting, " in Proc. Adv. Neural Inf. Process. Syst., vol. 22. Whistler, BC, Canada, 2009, pp. 629-633.
25. J. Bi et al., "AdaBoost on low-rank PSD matrices for metric learning, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Colorado Springs, CO, USA, 2011, pp. 2617-2624.
26. J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon, "Informationtheoretic metric learning, " in Proc. 24th Int. Conf. Mach. Learn., Corvallis, OR, USA, 2007, pp. 209-216.
27. M. Möller, "Dynamic time warping, " in Information Retrieval for Music and Motion. Berlin, Germany: Springer, 2007, pp. 69-84.
28. M. Liu and B. C. Vemuri, "A robust and efficient doubly regularized metric learning approach, " in Proc. 12th Eur. Conf. Comput. Vis., vol. 4. Florence, Italy, 2012, pp. 646-659.
29. K. Q. Weinberger and L. K. Saul, "Distance metric learning for large margin nearest neighbor classification, " J. Mach. Learn. Res., vol. 10, pp. 207-244, Feb. 2009.
30. S. Sun and Q. Chen, "Hierarchical distance metric learning for large margin nearest neighbor classification, " Int. J. Pattern Recognit. Artif. Intell., vol. 25, no. 7, pp. 1073-1087, 2011.
31. E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell, "Distance metric learning, with application to clustering with side-information, " in Proc. Adv. Neural Inf. Process. Syst., vol. 15. Vancouver, BC, Canada, 2002, pp. 505-512.
32. S. Sun and Q. Chen, "Kernel regression with a Mahalanobis metric for short-Term traffic flow forecasting, " in Intelligent Data Engineering and Automated Learning-IDEAL. Berlin, Germany: Springer, 2008, pp. 9-16.
33. T. M. Rath and R. Manmatha, "Lower-bounding of dynamic time warping distances for multivariate time series, " Center Intell. Inf. Retrieval, Univ. Massachusetts Amherst, Amherst, MA, USA, Tech. Rep. MM-40, 2002.
34. G. Al-Naymat, S. Chawla, and J. Taheri, "SparseDTW: A novel approach to speed up dynamic time warping, " in Proc. 8th Australasian Data Min. Conf., vol. 101. Melbourne, VIC, Australia, 2009, pp. 117-127.
35. M. Sugiyama, "Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis, " J. Mach. Learn. Res., vol. 8, pp. 1027-1061, May 2007.
36. J. Blitzer, K. Q. Weinberger, and L. K. Saul, "Distance metric learning for large margin nearest neighbor classification, " in Proc. Adv. Neural Inf. Process. Syst., Vancouver, BC, Canada, 2005, pp. 1473-1480.
37. J. Mei, M. Liu, H. R. Karimi, and H. Gao, "LogDet divergence based metric learning with triplet constraints and its applications, " IEEE Trans. Image Process., vol. 23, no. 11, pp. 4920-4931, Nov. 2014.
38. Q. Wang, P. C. Yuen, and G. Feng, "Semi-supervised metric learning via topology preserving multiple semi-supervised assumptions, " Pattern Recognit., vol. 46, no. 9, pp. 2576-2587, 2013.
39. P. Jain, B. Kulis, I. S. Dhillon, and K. Grauman, "Online metric learning and fast similarity search, " in Proc. Adv. Neural Inf. Process. Syst., Whistler, BC, Canada, 2008, pp. 761-768.
40. I. S. Dhillon and J. A. Tropp, "Matrix nearness problems with Bregman divergences, " SIAM J. Matrix Anal. Appl., vol. 29, no. 4, pp. 1120-1146, 2007.
41. B. Kulis, M. Sustik, and I. Dhillon, "Learning low-rank kernel matrices, " in Proc. 23rd Int. Conf. Mach. Learn., Pittsburgh, PA, USA, 2006, pp. 505-512.
42. J. Dhillon, "Differential entropic clustering of multivariate Gaussians, " in Proc. Adv. Neural Inf. Process. Syst., vol. 19. Vancouver, BC, Canada, 2007, pp. 337-344.