On the behavior of MDL denoising

AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics

Volumn , Issue , 2005, Pages 309-316

  Roos, Teemu ^a   Myllymäki, Petri ^a   Tirri, Henry ^b  

^a UNIVERSITY OF HELSINKI   (Finland)

^b NOKIA RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DE-NOISING; DENOISING METHODS; MINIMUM DESCRIPTION LENGTH PRINCIPLE; OVERFITTING; RENORMALIZATION; WAVELET DENOISING;

ARTIFICIAL INTELLIGENCE;

EID: 84862291560     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Abramovich, F., Bailey, T., & Sapatinas, T. 2000. Wavelet analysis and its statistical applications. Journal of the Royal Statistical Society, Series D, 49(1), 1-29.
2. Barron, A., Rissanen, J., & Yu, B. 1998. The minimum description length principle in coding and modeling. IEEE Transactions on Information Theory, 44(6), 2743-2760.
3. Chang, G., Yu, B., & Vetterli, M. 2000. Adaptive wavelet thresholding for image denoising and compression. IEEE Transactions on Image Processing, 9(9), 1532-1546.
4. Daubechies, I. 1992. Ten Lectures on Wavelets. Society for Industrial & Applied Mathematics (SIAM), Philadelphia, PA.
5. Donoho, D., & Johnstone, I. 1991. Minimax estimation via wavelet shrinkage. Tech. rept., Stanford University.
6. Donoho, D., & Johnstone, I. 1994. Ideal spatial adaptation via wavelet shrinkage. Biometrika, 81(3), 425-455.
7. Fodor, I.K., & Kamath, C. 2003. Denoising through wavelet shrinkage: an empirical study. Journal of Electronic Imaging, 12(1), 151-160.
8. Foster, D.P., & Stine, R.A. 2001. The competitive complexity ratio. Proceedings of the 2001 Conference on Information Sciences and Systems, 1-6.
9. Foster, D.P., & Stine, R.A. 2005. The contribution of parameters to stochastic complexity. To appear in: Grünwald, P., Myung, I.J., & Pitt, M. (eds), Advances in Minimum Description Length: Theory and Applications, MIT Press, Cambridge, MA.
10. Grünwald, P. 2005. A Tutorial introduction to the minimum description length principle. To appear in: Grünwald, P., Myung, I.J., & Pitt, M. (eds), Advances in Minimum Description Length: Theory and Applications, MIT Press, Cambridge, MA.
11. Hansen, M., & Yu, B. 2000. Wavelet thresholding via MDL for natural images. IEEE Transactions on Information Theory, 46(7), 1778-1788.
12. Mallat, S. 1989. A Theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674-693.
13. Ojanen, J., Miettinen, T., Heikkonen, J., & Rissanen, J. 2004. Robust denoising of electrophoresis and mass spectrometry signals with minimum description length principle. FEBS Letters, 570(1-3), 107-113.
14. Rissanen, J. 1996. Fisher information and stochastic complexity. IEEE Transactions on Information Theory, 42(1), 40-47.
15. Rissanen, J. 2000. MDL denoising. IEEE Transactions on Information Theory, 46(7), 2537-2543.
16. Ruggeri, F., & Vidakovic, B. 1999. A Bayesian decision theoretic approach to the choice of thresholding parameter. Statistica Sinica, 9(1), 183-197.
17. Shtarkov, Yu M. 1987. Universal sequential coding of single messages. Problems of Information Transmission, 23(3), 3-17.
18. Weaver, J.B., Yansun, X., Healy, D.M. Jr., & Cromwell, L.D. 1991. Filtering noise from images with wavelet transforms. Magnetic Resonance in Medicine, 21(2), 288-295.