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16,296
A Simple Threshold Nonlinearity For Blind Signal Separation
 in Proc. ISCAS
, 2000
"... A computationally simple nonlinearity in the form of a threshold device is shown to serve as contrast function in blind signal separation. Convergence is shown to be robust, fast, and comparable with that of more complex polynomial nonlinearities. Together with the known signum nonlinearity for supe ..."
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Cited by 8 (8 self)
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A computationally simple nonlinearity in the form of a threshold device is shown to serve as contrast function in blind signal separation. Convergence is shown to be robust, fast, and comparable with that of more complex polynomial nonlinearities. Together with the known signum nonlinearity
A Simple Threshold Nonlinearity For Blind Separation Of SubGaussian Signals
 IEEE Intl. Symp. on Circuits and Systems ISCAS 2000
, 2000
"... A computationally simple nonlinearity in the form of a threshold device for the blind separation of subGaussian signals is derived. Convergence is shown to be robust, fast, and comparable to that of more complex polynomial nonlinearities. Together with the known signum nonlinearity for superGaussi ..."
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Cited by 1 (0 self)
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A computationally simple nonlinearity in the form of a threshold device for the blind separation of subGaussian signals is derived. Convergence is shown to be robust, fast, and comparable to that of more complex polynomial nonlinearities. Together with the known signum nonlinearity for super
A Singular Value Thresholding Algorithm for Matrix Completion
, 2008
"... This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task of reco ..."
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Cited by 555 (22 self)
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of recovering a large matrix from a small subset of its entries (the famous Netflix problem). Offtheshelf algorithms such as interior point methods are not directly amenable to large problems of this kind with over a million unknown entries. This paper develops a simple firstorder and easy
Thresholding of statistical maps in functional neuroimaging using the false discovery rate.
 NeuroImage
, 2002
"... Finding objective and effective thresholds for voxelwise statistics derived from neuroimaging data has been a longstanding problem. With at least one test performed for every voxel in an image, some correction of the thresholds is needed to control the error rates, but standard procedures for mult ..."
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Cited by 521 (9 self)
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Finding objective and effective thresholds for voxelwise statistics derived from neuroimaging data has been a longstanding problem. With at least one test performed for every voxel in an image, some correction of the thresholds is needed to control the error rates, but standard procedures
Nonlinear total variation based noise removal algorithms
, 1992
"... A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using the g ..."
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Cited by 2271 (51 self)
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the gradientprojection method. This amounts to solving a time dependent partial differential equation on a manifold determined by the constraints. As t ~ 0o the solution converges to a steady state which is the denoised image. The numerical algorithm is simple and relatively fast. The results appear
A Limited Memory Algorithm for Bound Constrained Optimization
 SIAM JOURNAL ON SCIENTIFIC COMPUTING
, 1994
"... An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based ..."
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Cited by 572 (9 self)
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An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based
Systematic Nonlinear Planning
 In Proceedings of the Ninth National Conference on Artificial Intelligence
, 1991
"... This paper presents a simple, sound, complete, and systematic algorithm for domain independent STRIPS planning. Simplicity is achieved by starting with a ground procedure and then applying a general, and independently verifiable, lifting transformation. Previous planners have been designed directly ..."
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Cited by 449 (3 self)
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This paper presents a simple, sound, complete, and systematic algorithm for domain independent STRIPS planning. Simplicity is achieved by starting with a ground procedure and then applying a general, and independently verifiable, lifting transformation. Previous planners have been designed directly
Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming
 Journal of the ACM
, 1995
"... We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds the solution ..."
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Cited by 1211 (13 self)
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We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds
Initial Conditions and Moment Restrictions in Dynamic Panel Data Models
 Journal of Econometrics
, 1998
"... Estimation of the dynamic error components model is considered using two alternative linear estimators that are designed to improve the properties of the standard firstdifferenced GMM estimator. Both estimators require restrictions on the initial conditions process. Asymptotic efficiency comparisons ..."
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Cited by 2393 (16 self)
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comparisons and Monte Carlo simulations for the simple AR(1) model demonstrate the dramatic improvement in performance of the proposed estimators compared to the usual firstdifferenced GMM estimator, and compared to nonlinear GMM. The importance of these results is illustrated in an application
Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging
 MAGNETIC RESONANCE IN MEDICINE 58:1182–1195
, 2007
"... The sparsity which is implicit in MR images is exploited to significantly undersample kspace. Some MR images such as angiograms are already sparse in the pixel representation; other, more complicated images have a sparse representation in some transform domain–for example, in terms of spatial finit ..."
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Cited by 538 (11 self)
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due to random undersampling add as noiselike interference. In the sparse transform domain the significant coefficients stand out above the interference. A nonlinear thresholding scheme can recover the sparse coefficients, effectively recovering the image itself. In this article, practical incoherent
Results 1  10
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16,296