Retrospective mutiple change-point estimation with kernels

IEEE Workshop on Statistical Signal Processing Proceedings

Volumn , Issue , 2007, Pages 768-772

  Harchaoui, Zaïd ^a   Cappé, Olivier ^a  

^a INSTITUT TELECOM   (France)

Author keywords

[No Author keywords available]

Indexed keywords

SIGNAL PROCESSING; SYSTEMS ENGINEERING; TECHNICAL PRESENTATIONS;

CHANGE POINTS; COMPLEX SIGNALS; COMPUTATIONALLY EFFICIENT; DYNAMIC PROGRAMMING ALGORITHMS; KERNEL MAPPINGS; PARAMETRIC MODELS; PIECE WISES; REPRODUCING KERNELS; VERSATILE TOOLS;

CONFORMAL MAPPING;

EID: 47849096403     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SSP.2007.4301363     Document Type: Conference Paper

References (20)

1. D. Barry and J. Hartigan. Product partition models for change point problems. Annals of Statistics, 20:260-279, 1992.
2. R. Bellman. On the approximation of curves by line segments using dynamic programming. Communications of the ACM, 4(6), 1961.
3. D. Bosq. Linear Processes in Function Spaces: Theory and Applications. Springer, 2000.
4. L. Boysen, A. Kempe, A. Munk, V. Liebscher, and O. Wittich. Consistencies and rates of convergence of jump penalized least squares estimators. Annals of Statistics, In revision.
5. B. Brodsky and B. Darkhovsky. Non-parametric statistical diagnosis: problems and methods. Kluwer Academic Publishers, 2000.
6. S. Chiappa and J. d. R. Millan. Data set V mental imagery, multi-class, 2004. http://ida.first.fraunhofer.de/projects/bci/competition_iii/.
7. M. Csörgö and L. Horváth. Limit Theorems in Change-Point Analysis. Wiley, 1998.
8. M. Davy, F. Désobry, and S. Canu. Estimation of minimum measure sets in reproducing kernel hilbert spaces and applications. IEEE ICASSP, 2006.
9. F. Désobry, M. Davy, and C. Doncarli. An online kernel change detection algorithm. IEEE Transactions on Signal Processing, 53(8):2961-2974, August 2005.
10. P. Fearnhead. Exact and efficient bayesian inference for multiple changepoint problems. Statistics and Computing, 16:203-213, 2006.
11. W. D. Fisher. On grouping for maximum homogeneity. Journal of the American Statistical Society, 53:789-798, 1958.
12. K. Fukumizu, F. Bach, and A. Gretton. Statistical convergence of kernel canonical correlation analysis. Journal of Machine Learning Research, 8(8), 2007.
13. O. Gillet, S. Essid, and G. Richard. On the correlation of automatic audio and visual segmentation of music videos. IEEE Transactions on Circuits and Systems for Video Technology, 2007.
14. W. Härdle and L. Simar. Applied Multivariate Statistical Analysis (2nd ed.). Springer, 2007.
15. B. Jackson, J. Scargle, D. Barnes, A. Sundararajan, A. Alt, P. Gioumousis, E. Gwin, P. Sangtrakulcharoen, L. Tan, and T. T. Tsai. An algorithm for optimal partitioning of data on an interval. IEEE Signal Processing Letters, 12:105-108, 2005.
16. S. M. Kay. Fundamentals of statistical signal processing: detection theory. Prentice-Hall, Inc., 1993.
17. M. Lavielle and E. Moulines. Least-squares estimation of an unknown number of shifts in a time series. Journal of time series analysis, 21(1):33-59, 2000.
18. C.-B. L. Lee. Estimating the number of change-points in a sequence of independent random variables. Statistics and Probability Letters, 25:241-248, 1995.
19. B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, 2002.
20. J. Shawe-Taylor and N. Cristianini. Kernel Methods for Pattern Analysis. Cambridge Univ. Press, 2004.