Ronald R. Coifman and Mladen Victor Wickerhauser, "Entropy based algorithms for best basis selection," IEEE Transactions on Information Theory, vol. 32, pp. 712--718, Mar. 1992. Home/Search Document Details and Download
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Spread Spectrum Interference Suppression Using Adaptive - Time-Frequency Tilings Brian (1997) (Correct)
....evenly over all coe#cients and is thus mostly preserved by the interference excision. Existing time frequency methods can be e#ective for certain types of interferers, but they may struggle with other types of nonstationary interference or with multiple interferers. Wavelet packet transforms [4] can adapt an orthogonal subband decomposition to better match some interferers. Adaptive time frequency excision has recently been developed in [5] E#ectively, this method adapts an orthogonal subband decomposition in either time or frequency to match the subbands to the frequency (or time) ....
....localization. Within this class of tilings, there exists a wavelet packet basis which, in terms of some cost measure, is best at concentrating an interferer in as few coe#cients as possible. This wavelet packet basis can be obtained using an e#cient algorithm developed by Coifman and Wickerhauser [4] that requires O(log N) operations per sample for a length N input signal. 2.2. Arbitrary Tree Structured Time Frequency Tilings One disadvantage of the wavelet packet transform is that its frequency localization is constant over time. If the signal block is not stationary, it may be desirable ....
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R. R. Coifman and M. V. Wickerhauser, "Entropybased algorithms for best basis selection," IEEE Trans. Info. Theory, vol. 38, pp. 713--718, March 1992.
Laborat orio VISGRAF - Instituto De Matem (Correct)
....Function #### to be folded. b) The modulated version of ####. c) The flipped portion of the modulated #### that was outside the interval. d) The portion of #### inside the interval. e) The flipped portion added to the inside portion of the ####. wavelet packet tree, introduced in [15], and may be used to search the local cosine basis that is more adapted to the signal characteristics. If we consider a time interval ##### as the signal extent, we can divide into in # # # intervals, # p # ## p;j # # p##;j # where # p;j # ## # for # # # # # , which has length # p ## ....
R. R. Coifman and M. V. Wickerhauser. Entropybased algorithms for best basis selection. IEEE Transactions on Information Theory, 38(2):713--718, March 1992.
Locally Stationary Covariance and Signal Estimation .. - Donoho, Mallat.. (2001) (Correct)
....replacing the small ones with 0. When the family of models is constructed from dictionaries of orthonormal bases having a tree structure, like a local cosine dictionary, the best penalized model is computed with a fast dynamic programming algorithm similar to the one of Coifman and Wickerhauser [6], which takes advantage of the additivity property of the penalization cost for partial models. A partial model M is characterized by an orthonormal basis B of a subspace V of C , as opposed to a basis of the whole space, and a segmentation S of B. The cost of this model is de ned by Cost(M) ....
....of B = B 1 [ B 2 obtained by segmenting B 1 with S 1 and B 2 with S 2 . Clearly Cost(M) Cost(M 1 ) Cost(M 2 ) 22) Using this additive property in a tree dictionary, one can now identify the model that minimizes the cost with the fast bottom up strategy of Coifman and Wickerhauser [6]. To any node of the local cosine tree, is associated a family of local cosine vectors B p which generates a vector space V p . Let M p be the optimal model of V p which minimizes Cost(M) among all models M de ned by (B; S) where B is a local cosine basis of V p . One possible candidate is ....
R.R. Coifman and M.V. Wickerhauser, Entropy-based algorithms for best-basis selection,IEEE Trans. Inf. Theor, vol 38, pp 713-718, 1992.
Image Coding Subject to Constraints - Frajka (Correct)
....vertical filtering of the coefficients in that subband. The above octave band decomposition assumes that most of the energy is contained in the low frequency coefficients. For some images this iteration on the LL band may not provide the best energy compaction; a best basis selection algorithm ([18, 65]) could be used to optimize the decomposition structure. HH HH LH HL HL 2 2 1 1 1 Figure 2.3: The 2 level wavelet decomposition with subband notation. Most natural images are smooth and slowly varying. One would expect an exact reconstruction subband coding scheme to rely on orthonormal ....
R. R. Coifman and M. V. Wicherhauser. Entropy-based algorithms for best basis selection. IEEE Transactions on Information Theory, 38(2):713--718, March 1992.
A New Basis Selection Paradigm for Wavelet Packet Image.. - Rajpoot, Wilson, Meyer.. (2001) (2 citations) Self-citation (Coifman) (Correct)
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R.R. Coifman and M.V. Wickerhauser, "Entropy-based algorithms for best basis selection," IEEE Trans. on Info. Th., vol. 38, no. 2, pp. 713--718, Mar. 1992.
Adaptive Wavelet Packet Basis Selection for Zerotree.. - Rajpoot, Wilson.. (2003) Self-citation (Coifman) (Correct)
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R. R. Coifman and M. V. Wickerhauser, "Entropy-based algorithms for best basis selection," IEEE Trans. Inform. Theory, vol. 38, pp. 713--718, Mar. 1992.
Information Cost Functions - Sikic, Wickerhauser Self-citation (Wickerhauser) (Correct)
....analysis of X form a discrete, in fact finite, library B whose members are the many combinations of relatively few pieces. With decomposability comes a low complexity divide and conquer algorithm for finding the minimizing basis for a fixed informa tion cost function H, and also for coding it [2]. Reference [9] pages 310ff, describes the wavelet packet al..gorithm in detail. By Theorem 3.1, minimizing any single H locates the sole candidate for best basis. Since H(p) H(p ) this candidate can be identified without rearrangement. By Theorem 3.3, that candidate is in fact a best basis if ....
....than the one in Theorem 4.1, one that avoids subexponential extensions and thus has a simpler dependence on d, and is always finite if H(f,p) is finite. For example, is it possible to have an estimate of the form H(g,p) Cg o f (p) 42) where f: R (0, 1) satisfies 0 r f(t) i for all t [ 2], and C and are some fixed positive numbers. The idea is to map nP back into the domain (0, 1) of g, while making sure the upper bound avoids the potential infinity at g(0) at least for 1. But no such estimate can hold for all concavable nonnegative nonincreasing g, as the following shows: ....
R. R. Coifman and M. V. Wickerhauser. Entropy based algorithms for best basis selection. IEEE Transactions on Information Theory, 32:712-718, March 1992.
Adaptive Wavelet Transforms via Lifting - Roger Claypoole Richard (Correct)
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Ronald R. Coifman and Mladen Victor Wickerhauser, "Entropy based algorithms for best basis selection," IEEE Transactions on Information Theory, vol. 32, pp. 712--718, Mar. 1992.
Nonlinear processing of a shift invariant DWT for noise.. - Lang Guo Odegard (Correct)
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R. R. Coifman and M. V. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Trans. Inform. Theory, 38(2):1713--1716, 1992.
Measuring Time-Frequency - Information Content Using (Correct)
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R. Coifman and V. Wickerhauser, "Entropy-based algorithms for best basis selection," IEEE Trans. Inform. Theory, vol. IT--38, pp. 713--718, Mar. 1992.
Pseudo Power Signatures For - Nonstationary Signal Analysis (Correct)
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R. Coifman and V. Wickerhauser, " Entropy-based Algorithms For Best Basis Selection," IEEE Trans. on Information Theory, Vol. 38, No. 2, Mar. 1992.
Measuring Time-Frequency - Information Content Using (Correct)
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R. Coifman and V. Wickerhauser, "Entropy-based algorithms for best basis selection," IEEE Trans. Inform. Theory, vol. IT--38, pp. 713--718, Mar. 1992.
Appears in: IEEE Trans. on CAS II - April 1994 - Optimal Wavelet Representation (Correct)
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R. R. Coifman and M. V. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Trans. Inform. Theory, 38(2):1713--1716, 1992.
Unknown - (Correct)
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R. R. Coifman and M. V. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Trans. Inform. Theory, 38(2):1713--1716, 1992.
Bio-CONCUR 2004 Preliminary Version - Multiple Biological Model (Correct)
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R. R. Coifman and M. V. Wickerhauser. Entropy-based Algorithms for Best Basis Selection. I.E.E.E. Transactions on Information Theory, 38(2), 1992.
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Coifman, R. R., and M. V. Wickerhauser (1992), Entropy based algorithms for best basis selection, IEEE Trans. on Inf. Theory, 32, 712--718.
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R. Coifman and M. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Trans. Information Theory, 38:713--718, March 1992.
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R.R. Coifman and M.V. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Transactions on Information Theory, 38(2):713--718, March 1992.
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R. R. Coifman and M. V. Wickerhauser, "Entropy-based algorithms for best basis selection," IEEE Trans. Inform. Theory, vol. 38, no. 2, pp. 713--718, Mar. 1992.
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R.R. Coifman and M.V. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Trans. on Info. Th., 38(2):713--718, Mar. 1992.
Local Discriminant Wavelet Packet Basis - For Texture Classification (2003) (Correct)
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R.R. Coifman and M.V. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Trans. on Info. Th., 38(2):713--718, Mar. 1992.
Image Denoising Using Adaptive Subband Decomposition - Gezici, Yilmaz, Gerek.. (2001) (Correct)
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Ronald R. Coifman and Mladen Victor Wickerhauser, "Entropy-Based Algorithms for Best Basis Selection," IEEE Transactions on Information Theory, Vol. 38, No. 2, pp.713-718, March 1992.
Sparse Representations for Image Decompositions - Geiger, Liu, Donahue (1999) (Correct)
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R. Coifman and V. Wickerhauser, "Entropy-based Algorithms for Best Basis Selection," IEEE Transactions on Information Theory, vol. 38, no. 2, 1992. 22
Periodicity Transforms - Sethares, Staley (1999) (4 citations) (Correct)
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R. R. Coifman and M. V. Wickerhauser, "Entropy-based algorithms for best-basis selection," IEEE Trans. Inform. Theory, vol. 38, Mar. 1992.
Alternative Local Discriminant Bases Using Empirical Expectation .. - Fossgaard (Correct)
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Ronald R. Coifman and Mladen V. Wickerhauser. Entropy-based Algorithms For Best Basis Selection . IEEE Trans. Inform. Theory, 38(2):713--719, 1992.
Image Recognition with Occlusions - Liu, Donahue, Geiger, Hummel (1996) (Correct)
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R. Coifman and V. Wickerhauser, "Entropy-based Algorithms for Best Basis Selection ", IEEE Transactions on Information Theory, vol. 38, no. 2, 1992.
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R. R. Coifman and M. V. Wickerhauser, "Entropy-based algorithms for best basis selection," IEEE Trans. Inform. Theory, vol. 38, pp. 713--718, Mar. 1992.
Wavelet Footprints: Theory, Algorithms, and Applications - Dragotti, Vetterli (2003) (3 citations) (Correct)
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R. Coifman and M. Wickerhauser, "Entropy-based algorithms for best basis selection," IEEE Trans. Inform. Theory, vol. 38, pp. 713--718, Mar. 1992.
Data Compression and Harmonic Analysis - Donoho, Vetterli, DeVore.. (1998) (19 citations) (Correct)
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R. R. Coifman and M. V. Wickerhauser, "Entropy-based algorithms for best basis selection," vol. 38, no. 2, pp. 713--718, Mar. 1992.
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R. Coifman and V. Wickerhauser, "Entropy-based Algorithms for Best Basis Selection," IEEE Transactions on Information Theory, vol. 38, no. 2, 1992. 22
Wavelets, Approximation, and Compression - Vetterli (2001) (5 citations) (Correct)
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R.R. Coifman and M.V. Wickerhauser, "Entropy-based algorithms for best basis selection," IEEE Trans. Inform. Theory (Special Issue on Wavelet Transforms and Multiresolution Signal Analysis), vol. 38, pp. 713-718, Mar. 1992.
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R. Coifman and M. Wickerhauser, "Entropy-based algorithms for best basis selection," IEEE Trans. Inform. Theory, vol. 38, pp. 713--718, Mar. 1992.
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R.R.Coifman and Mladen Victor Wickerhauser, "Entropy-based algorithms for best basis selection", IEEE Trans. Information Theory, v38, n2, March, pp713-718, 1992.
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R. R. Coifman and M. V. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Trans. on Information Theory, 38(2):713---718, March 1992.
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R. R. Coifman and M. V. Wickerhauser, "Entropy based algorithms for best basis selection," IEEE Trans. Inf. Theory 38, 713--718 (1992).
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R. R Coifman, and M.V. Wickerhauser, "Entropy-Based algorithms for best basis selection", IEEE Trans. on Information Theory, vol.38(2), p.713~718, 1992.
Applications of Multiwavelets to Image Compression - Martin (1999) (1 citation) (Correct)
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R. R. Coifman and M. V. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Trans. on Information Theory, 38(2):713--718, March 1992.
Multiscale Detection of - Transiently Evoked Otoacoustic (2004) (Correct)
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R. R. Coifman and M. V. Wickerhauser, "Entropy-based algorithms for best basis selection," IEEE Trans. Inf. Theory, vol. 38, pp. 713--718, 1992.
Basis Pursuit - Chen (1995) (18 citations) (Correct)
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R.R. Coifman and M.V. Wickerhauser. Entropy-based algorithms for best-basis selection. IEEE Transactions on Information Theory, vol. 38, pp. 713--718, 1992.
Statistical Modeling and Conceptualization of Visual Patterns - Zhu (2003) (1 citation) (Correct)
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R.R. Coifman and M.V. Wickerhauser, "Entropy Based Algorithms for Best Basis Selection," IEEE Trans. Information Theory, vol. 38, pp. 713-718, 1992.
Sparse Geometric Image Representations with Bandelets - Le Pennec, Mallat (2004) (4 citations) (Correct)
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R. R. Coifman and M. V. Wickerhauser, "Entropy-based algorithms for best basis selection." IEEE Trans. Inform. Theory, vol. 38, no. 2, pp. 713--718, Mar. 1992.
Approximation of Functions over Redundant.. - Gilbert.. (2002) (6 citations) (Correct)
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R. Coifman and M. V. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Trans. Inform. Theory, 38(2), March 1992.
Signal-Adaptive Robust Time-Varying Wiener Filters: - Best Subspace Selection (Correct)
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R. R. Coifman and M. Wickerhauser, "Entropy-based algorithms for best basis selection," IEEE Trans. Inf. Theory, vol. 38, pp. 713--718, March 1992.
Best-Bases Feature Extraction Algorithms for - Classification Of Hyperspectral (Correct)
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R. R. Coifman and M. V. Wickerhauser, "Entropy-based algorithms for best basis selection," IEEE Trans. Inform. Theory, vol. 38, pp. 713--719, Mar. 1992.
Frequency-Shift-Invariant Orthonormal Wavelet Packet - Representations Brian Krongold (1997) (Correct)
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R. R. Coifman and W. V. Wickerhauser, "Entropybased algorithms for best basis selection," IEEE Trans. Inform. Theory, vol. 38, pp. 713--718, March 1993.
Wavelet Footprints and Frames for Signal Processing and.. - Dragotti (2002) (Correct)
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R. Coifman and M. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Trans. Information Theory, 38:713--718, March 1992.
Some Experiments on Independent Component Analysis - Of Non-Gaussian Processes (Correct)
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R. R. Coifman and M. V. Wickerhauser (1992) "Entropybased algorithms for best-basis selection", IEEE Trans. Info. Theory, 38, pp. 713-718.
Wavelet Footprints: Theory, Algorithms and Applications - Dragotti, Vetterli (2002) (3 citations) (Correct)
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R. Coifman and M. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Trans. Information Theory, 38:713--718, March 1992.
Image Recognition with Occlusions - Liu, Donahue, Geiger, Hummel (1996) (Correct)
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R. Coifman and V. Wickerhauser, "Entropy-based Algorithms for Best Basis Selection ", IEEE Transactions on Information Theory, vol. 38, no. 2, 1992.
An Equivalence Between Sparse Approximation - And Support Vector (Correct)
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R.R. Coifman and M.V. Wickerhauser. Entropy-based algorithms for best-basis selection. IEEE Transactions on Information Theory, 38:713--718, 1992.
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