Principal curve time warping

IEEE Transactions on Signal Processing

Volumn 57, Issue 6, 2009, Pages 2041-2049

  Ozertem, Umut ^a   Erdogmus, Deniz ^b  

^a YAHOO RESEARCH   (United States)

^b Northeastern University   (United States)

Author keywords

Kernel density estimation (KDE); Principal curves; Signal denoising; Time warping

Indexed keywords

APPLICATION AREA; KERNEL DENSITY ESTIMATION (KDE); LOW SIGNAL-TO-NOISE RATIO; MACHINE-LEARNING; NOISE LEVELS; NOISE ROBUSTNESS; PRINCIPAL CURVE; PRINCIPAL CURVES; PROBLEM DEFINITION; SIGNAL DENOISING; TIME WARPING; TIME WARPING METHODS;

ACOUSTIC INTENSITY; COMPUTATIONAL COMPLEXITY; SIGNAL PROCESSING; SIGNAL TO NOISE RATIO; WEAVING;

TIME SERIES ANALYSIS;

EID: 66949116015     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2009.2016268     Document Type: Article

References (28)

1. H. Sakoe and S. Chiba, "A dynamic programming approach to continuous speech recognition," in Proc. Int. Congress Acoust., Budapest, Hungary, 1971.
2. M. R. Sabnur and L. R. Rabiner, "A statistical decision approach to the recognition of connected digits," IEEE. Trans. Acoust., Speech, Signal Process., vol. ASSP-24, pp. 550-558, 1976.
3. J. S. Bridle, "Stochastic models and template matching: Some important relationships between two apparently different techniques in speech processing," Proc. Inst. Acoust., vol. 6, pp. 452a-452h, 1984.
4. L. Rabiner, A. Rosenberg, and S. Levinson, "Consideration in dynamic time warping algorithms for discrete word recognition," IEEE Trans. Acoust., Speech Signal Process., vol. ASSP-26, pp. 575-582, 1978.
5. D. Berndt and J. Clifford, "Using dynamic timewarping to find patterns in time series," in Workshop on Knowledge Discovery in Databases, 1994.
6. C. Myers, L. Rabnier, and A. Rosenberg, "Performance tradeoffs in dynamic time warping algorithms isolated word recognition," IEEE Trans. Acoust., Speech Signal Process., vol. ASSP-28, pp. 623-635, 1980.
7. C. A. Ratanamahatana and E. Keogh, "Everything you know about dynamic time warping is wrong," in Proc. Third Workshop on Mining Temporal and Sequential Data, Seattle, WA, 2004.
8. E. J. Keogh and M. J. Pazzani, "Derivative dynamic time warping," in Proc. First SIAM Int. Conf. Data Mining, 2001.
9. E.Yaniv and D. Burshtein, "An enhanced dynamic time warping model for improved estimation of DTW parameters," IEEE Trans. Speech Audio Process., vol. 11, 2003.
10. A. Pikrakis, S. Theodoridis, and D. Kamarotos, "Recognition of isolated musical patterns using context dependent dynamic timewarping," IEEE Trans. Speech Audio Process., vol. 11, 2003.
11. T. Hastie, "Principal curves and surfaces," Ph.D., Stanford Univ. , Stanford, CA, 1984.
12. T. Hastie and W. Stuetzle, "Principal curves," J. Amer. Statist. Assoc., vol. 84, pp. 502-516, 1989.
13. R. Tibshirani, "Principal curves revisited," Statist. Computat., vol. 2, pp. 183-190, 1992.
14. S. Sandilya and S. R. Kulkarni, "Principal curves with bounded turn," IEEE Trans. Inf. Theory, vol. 48, no. 10, pp. 2789-2793, 2002.
15. B. Kegl, A. Kryzak, T. Linder, and K. Zeger, "Learning and design of principal curves," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 3, pp. 281-297, 2000.
16. B. Kegl and A. Kryzak, "Piecewise linear skeletonization using principal curves," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 1, pp. 59-74, 2002.
17. D. C. Stanford and A. E. Raftery, "Finding curvilinear features in spatial point patterns: Principal curve clustering with noise," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 6, pp. 601-609, 2000.
18. D. Erdogmus and U. Ozertem, "Self-consistent locally defined principal surfaces," in Proc. Int. Conf. Acoust., Speech Signal Process., 2007, vol. 2, pp. II549-II552.
19. U. Ozertem and D. Erdogmus, "Local conditions for critical and principal manifolds," in IEEE Int. Conf. Acoust., Speech Signal Process., 2008, pp. 1893-1896.
20. L. Zelnik-Manor and P. Perona, "Self-tuning spectral clustering," Adv. Neural Inf. Process. Syst., vol. 17, 2004.
21. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. New York: Wiley, 2000.
22. R. Jenssen, T. Eltoft, D. Erdogmus, and J. C. Principe, "Some equivalences between kernel methods and information theoretic methods," J. VLSI Signal Process. Syst., vol. 45, no. 1-2, pp. 49-65, 2006.
23. B. W. Silverman, Density Estimation for Statistics and Data Analysis. London, U.K.: Chapman and Hall, 1986.
24. D. Comaniciu, "An algorithm for data-driven bandwidth selection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 2, pp. 281-288, 2003.
25. E. Parzen, "On the estimation of a probability density function and the mode," Ann. Math. Statist., vol. 32, pp. 1065-1076, 1962.
26. Y. Cheng, "Mean shift, mode seeking, and clustering," IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 8, pp. 790-799, 1995.
27. D. Comaniciu and P. Meer, "Mean shift: A robust approach towards feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, pp. 603-619, 2002.
28. E. Keogh, X. Xi, L. Wei, and C. A. Ratanamahatana, The UCR Time Series Classification/Clustering Homepage Publicly [Online]. Available: www.cs.ucr.edu/~eamonn/time_series_data/