Basis pursuit

The time-frequency and time-scale communities have recently developed an enormous number of over-complete signal dictionaries, wavelets, wavelet packets, cosine packets, Wilson bases, chirplets, warped bases, and hyperbolic cross bases being a few examples. Basis pursuit is a technique for decomposing a signal into an "optimal" superposition of dictionary elements. The optimization criterion is the l/sup 1/ norm of coefficients. The method has several advantages over matching pursuit and best ortho basis, including super-resolution and stability.<<ETX>>

[1]  G. Weiss,et al.  Extensions of Hardy spaces and their use in analysis , 1977 .

[2]  Nesa L'abbe Wu,et al.  Linear programming and extensions , 1981 .

[3]  Michael A. Saunders,et al.  LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares , 1982, TOMS.

[4]  Michael A. Saunders,et al.  Algorithm 583: LSQR: Sparse Linear Equations and Least Squares Problems , 1982, TOMS.

[5]  W. Steiger,et al.  Least Absolute Deviations: Theory, Applications and Algorithms , 1984 .

[6]  Narendra Karmarkar,et al.  A new polynomial-time algorithm for linear programming , 1984, Comb..

[7]  Michael A. Saunders,et al.  On projected newton barrier methods for linear programming and an equivalence to Karmarkar’s projective method , 1986, Math. Program..

[8]  M. Kojima,et al.  A primal-dual interior point algorithm for linear programming , 1988 .

[9]  Ingrid Daubechies,et al.  Time-frequency localization operators: A geometric phase space approach , 1988, IEEE Trans. Inf. Theory.

[10]  Christopher Heil,et al.  Continuous and Discrete Wavelet Transforms , 1989, SIAM Rev..

[11]  Robert J. Vanderbei,et al.  The AT&T KORBX® system , 1989, AT&T Technical Journal.

[12]  Jeffrey C. Lagarias,et al.  Power series variants of Karmarkar-type algorithms , 1989, AT&T Technical Journal.

[13]  J. Friedman,et al.  FLEXIBLE PARSIMONIOUS SMOOTHING AND ADDITIVE MODELING , 1989 .

[14]  H. Feichtinger Atomic characterizations of modulation spaces through Gabor-type representations , 1989 .

[15]  Ronald R. Coifman,et al.  Wavelet analysis and signal processing , 1990 .

[16]  K. Gröchenig Describing functions: Atomic decompositions versus frames , 1991 .

[17]  Y. Meyer Ondelettes sur l'intervalle. , 1991 .

[18]  Philip E. Gill,et al.  Numerical Linear Algebra and Optimization , 1991 .

[19]  Y. Meyer,et al.  Remarques sur l'analyse de Fourier à fenêtre , 1991 .

[20]  P. Gill,et al.  Solving Reduced KKT Systems in Barrier Methods for Linear and Quadratic Programming , 1991 .

[21]  G. Weiss,et al.  Littlewood-Paley Theory and the Study of Function Spaces , 1991 .

[22]  J. Freidman,et al.  Multivariate adaptive regression splines , 1991 .

[23]  R. Bracewell,et al.  Adaptive chirplet representation of signals on time-frequency plane , 1991 .

[24]  Nimrod Megiddo,et al.  On Finding Primal- and Dual-Optimal Bases , 1991, INFORMS J. Comput..

[25]  Thomas F. Coleman,et al.  A globally and quadratically convergent affine scaling method for linearℓ1 problems , 1992, Math. Program..

[26]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[27]  D. Donoho Superresolution via sparsity constraints , 1992 .

[28]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[29]  Ronald R. Coifman,et al.  Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.

[30]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.

[31]  I. Johnstone,et al.  Maximum Entropy and the Nearly Black Object , 1992 .

[32]  Edward H. Adelson,et al.  Shiftable multiscale transforms , 1992, IEEE Trans. Inf. Theory.

[33]  Simon Haykin,et al.  Adaptive chirplet transform: an adaptive generalization of the wavelet transform , 1992 .

[34]  R. DeVore,et al.  Compression of wavelet decompositions , 1992 .

[35]  B. Logan,et al.  Signal recovery and the large sieve , 1992 .

[36]  Thomas A. Manteuffel,et al.  A Comparison of Adaptive Chebyshev and Least Squares Polynomial Preconditioning for Hermitian Positive Definite Linear Systems , 1992, SIAM J. Sci. Comput..

[37]  I. Daubechies,et al.  Biorthogonal bases of compactly supported wavelets , 1992 .

[38]  Kannan Ramchandran,et al.  Tilings of the time-frequency plane: construction of arbitrary orthogonal bases and fast tiling algorithms , 1993, IEEE Trans. Signal Process..

[39]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[40]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[41]  Gunnar Peters,et al.  Wavelet probing for compression-based segmentation , 1993, Optics & Photonics.

[42]  Karlheinz Gröchenig,et al.  Acceleration of the frame algorithm , 1993, IEEE Trans. Signal Process..

[43]  Douglas L. Jones,et al.  Shear madness: new orthonormal bases and frames using chirp functions , 1993, IEEE Trans. Signal Process..

[44]  S. Mallat,et al.  Second generation compact image coding with wavelets , 1993 .

[45]  Robert J. Vanderbei,et al.  Commentary - Interior-Point Methods: Algorithms and Formulations , 1994, INFORMS J. Comput..

[46]  Roy E. Marsten,et al.  Feature Article - Interior Point Methods for Linear Programming: Computational State of the Art , 1994, INFORMS J. Comput..

[47]  Shie Qian,et al.  Signal representation using adaptive normalized Gaussian functions , 1994, Signal Process..

[48]  Michael A. Saunders,et al.  Commentary - Major Cholesky Would Feel Proud , 1994, INFORMS J. Comput..

[49]  D. Donoho On Minimum Entropy Segmentation , 1994 .

[50]  D. L. Donoho,et al.  Ideal spacial adaptation via wavelet shrinkage , 1994 .

[51]  Yuying Li,et al.  An Affine Scaling Algorithm for Minimizing Total Variation in Image Enhancement , 1994 .

[52]  I. Johnstone,et al.  Ideal denoising in an orthonormal basis chosen from a library of bases , 1994 .

[53]  Č. V. Stanojević,et al.  Fourier analysis : analytic and geometric aspects , 1994 .

[54]  Robert E. Bixby,et al.  Commentary - Progress in Linear Programming , 1994, INFORMS J. Comput..

[55]  Yurii Nesterov,et al.  Interior-point polynomial algorithms in convex programming , 1994, Siam studies in applied mathematics.

[56]  M. Victor Wickerhauser,et al.  Adapted wavelet analysis from theory to software , 1994 .

[57]  Michael J. Todd Commentary - Theory and Practice for Interior-Point Methods , 1994, INFORMS J. Comput..

[58]  D. Donoho Nonlinear Solution of Linear Inverse Problems by Wavelet–Vaguelette Decomposition , 1995 .

[59]  I. Johnstone,et al.  Wavelet Shrinkage: Asymptopia? , 1995 .

[60]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[61]  David L. Donoho,et al.  WaveLab and Reproducible Research , 1995 .

[62]  Young K. Truong,et al.  LOGSPLINE ESTIMATION OF A POSSIBLY MIXED SPECTRAL DISTRIBUTION , 1995 .

[63]  Gilbert Strang,et al.  Short wavelets and matrix dilation equations , 1995, IEEE Trans. Signal Process..

[64]  Ronald A. DeVore,et al.  Some remarks on greedy algorithms , 1996, Adv. Comput. Math..

[65]  Fadil Santosa,et al.  Recovery of Blocky Images from Noisy and Blurred Data , 1996, SIAM J. Appl. Math..

[66]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[67]  J. L. Nazareth,et al.  Cholesky-based Methods for Sparse Least Squares : The Benefits of Regularization ∗ , 1996 .