Piecewise smooth signal denoising via principal curve projections

Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Volumn , Issue , 2008, Pages 426-431

  Ozertem, Umut ^a   Erdogmus, Deniz ^b   Arikan, Orhan ^c  

^a YAHOO INC   (United States)

^b OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

^c BILKENT UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS; ROBOT LEARNING; TECHNICAL PRESENTATIONS;

COMMON PROBLEMS; DENOISED SIGNALS; DENOISING ALGORITHMS; GIBBS PHENOMENONS; NONPARAMETRIC; PIECE WISES; PIECEWISE SMOOTHES; PRINCIPAL CURVES; SIGNAL DENOISING; TOTAL VARIATIONS; WAVELET COEFFICIENTS;

SIGNAL PROCESSING;

EID: 58049156413     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MLSP.2008.4685518     Document Type: Conference Paper

References (16)

1. Simon Haykin, Adaptive filter theory (3rd ed.), Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1996.
2. Leonid I. Rudin, Stanley Osher, and Emad Fatemi, "Nonlinear total variation based noise removal algorithms," Phys. D, vol. 60, no. 1-4, pp. 259-268, 1992.
3. David C. Dobson and Fadil Santosa, "Recovery of blocky images from noisy and blurred data," SIAM Journal on Applied Mathematics, vol. 56, no. 4, pp. 1181-1198, 1996.
4. C. R. Vogel and M. E. Oman, "Iterative methods for total variation denoising," SIAM Journal on Scientific Computing, vol. 17, no. 1, pp. 227-238, 1996.
5. Albert Cohen and Jean-Pierre D'Ales, "Nonlinear approximation of random functions," SIAM Journal on Applied Mathematics, vol. 57, no. 2, pp. 518-540, 1997.
6. David L. Donoho, "De-noising by soft-thresholding," IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613-627, 1995.
7. Sylvain Durand and Jacques Froment, "Reconstruction of wavelet coefficients using total variation minimization," SIAM J. Sci. Comput., vol. 24, no. 5, pp. 1754-1767, 2002.
8. T. Hastie and W. Stuetzle, "Principal curves," Jour. Am. Statistical Assoc., vol. 84, pp. 502-516, 1989.
9. Deniz Erdogmus and Umut Ozertem, "Self-consistent locally defined principal surfaces," in Proceedings of International Conference on Acoustics, Speech, and Signal Processing, 2007, pp. II549-II552.
10. Umut Ozertem and Deniz Erdogmus, "Local conditions for critical and principal manifolds," in Proceedings of International Conference on Acoustics, Speech, and Signal Processing, 2008.
11. Y. Cheng, "Mean shift, mode seeking, and clustering," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 790-799, 1995.
12. Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification (2nd Edition), Wiley-Interscience, November 2000.
13. B. W. Silverman, Density Estimation for Statistics and Data Analysis, Chapman & Hall/CRC, April 1986.
14. Emanuel Parzen, "On estimation of a probability density function and mode," The Annals of Mathematical Statistics, vol. 33, no. 3, pp. 1065-1076, 1962.
15. Dorin Comaniciu, "An algorithm for data-driven bandwidth selection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 2, pp. 281-288, 2003.
16. Lihi Zelnik-Manor and Pietro Perona, "Self-tuning spectral clustering.," in NIPS, 2004.