Ridgelets: a key to higher-dimensional intermittency?

作者: Emmanuel J. Candès , David L. Donoho

DOI: 10.1098/RSTA.1999.0444

关键词:

摘要: In dimensions two and higher, wavelets can efficiently represent only a small range of the full diversity interesting behaviour. effect, are well adapted for pointlike phenomena, whe...

参考文章(15)
Sigurdur Helgason, Groups and geometric analysis ,(1984)
B. F. Logan, L. A. Shepp, Optimal reconstruction of a function from its projections Duke Mathematical Journal. ,vol. 42, pp. 645- 659 ,(1975) , 10.1215/S0012-7094-75-04256-8
Pierre Gilles Lemarié-Rieusset, Yves Meyer, Ondelettes et bases hilbertiennes. Revista Matematica Iberoamericana. ,vol. 2, pp. 1- 18 ,(1986) , 10.4171/RMI/22
David L. Donoho, Unconditional Bases and Bit-Level Compression Applied and Computational Harmonic Analysis. ,vol. 3, pp. 388- 392 ,(1996) , 10.1006/ACHA.1996.0032
Emmanuel J. Candès, Harmonic Analysis of Neural Networks Applied and Computational Harmonic Analysis. ,vol. 6, pp. 197- 218 ,(1999) , 10.1006/ACHA.1998.0248
David L. Donoho, Orthonormal ridgelets and linear singularities Siam Journal on Mathematical Analysis. ,vol. 31, pp. 1062- 1099 ,(2000) , 10.1137/S0036141098344403
David L. Donoho, Unconditional Bases Are Optimal Bases for Data Compression and for Statistical Estimation Applied and Computational Harmonic Analysis. ,vol. 1, pp. 100- 115 ,(1993) , 10.1006/ACHA.1993.1008
M Holschneider, Inverse Radon transforms through inverse wavelet transforms Inverse Problems. ,vol. 7, pp. 853- 861 ,(1991) , 10.1088/0266-5611/7/6/008