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Showing 1 to 12 of 12 for “"Basis Pursuit"”.
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Approximation of signals and functions in high dimensions with low dimensional structure: finite-valued sparse signals and generalized ridge functions
… that incorporates a finite values prior into basis pursuit, which is one classical reconstruction strategy in compressed sensing. In particular, we address unipolar binary and bipolar ternary sparse signals. We show that phase transition takes place earlier than using the classical basis …
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On fundamental computational barriers in the mathematics of information
… This includes computing the minimisers to basis pursuit, linear programming, lasso and image deblurring as well as finding an optimal neural network given training data. These results are somewhat paradoxical given the success that existing algorithms exhibit when tackling these problems …
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Innovative methods for the reconstruction of new generation satellite remote sensing images
… three different strategies (orthogonal matching pursuit, basis pursuit and a genetic algorithm solution) for the reconstruction of cloud-contaminated images; iv) a complete processing chain which exploits a support vector machine (SVM) classification and morphological filters for the detection …
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Iterative Memoryless Non-linear Estimators of Correlation for Complex-Valued Gaussian Processes that Exhibit Robustness to Impulsive Noise
… noise suppression technique is developed using basis pursuit and a priori atom weighting derived from the newly developed iterative estimators. This new technique is proposed as an alternative to the robust filter cleaner, a Kalman filter-like approach that relies on linear prediction residuals …
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Fast superresolution based on a network structure trained using sparse coding
… representations obtained from either ISTA, the Basis Pursuit, or any other L1 regularization solver such as LASSO or LARS, and the regression function obtained by training on the sparse vector obtained from the sparse recovery algorithm. The error function is minimized with respect to the three …
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Generalizations of the Alternating Direction Method of Multipliers for Large-Scale and Distributed Optimization
… and report its performance on very large-scale basis pursuit problems with distributed data.
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First Order Methods for Large-Scale Sparse Optimization
… methods for the following problem classes: Basis Pursuit (BP) in compressed sensing, Matrix Rank Minimization, Principal Component Pursuit (PCP) and Stable Principal Component Pursuit (SPCP) in principal component analysis. These problems have applications in signal and image processing, …
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Efficient tranceiver [sic] techniques for interference and fading mitigation in wireless communication systems
… implementation designs. To this end, building on basis pursuit and matching pursuit techniques new equalization schemes have been proposed that exhibit considerable computational savings, increased performance properties and short training sequence requirements. Our main contribution for this part …
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Uncertainty Quantification in Earth System Models Using Polynomial Chaos Expansions
… These PC surrogate models are constructed using Basis Pursuit DeNoising (BPDN) methodology, and their performance is assessed through various statistical measures. A global sensitivity analysis is then performed to quantify the impact of individual random sources as well as their interactions on …
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Use of the Traffic Speed Deflectometer for Concrete and Composite Pavement Structural Health Assessment: A Big-Data-Based Approach Towards Concrete and Composite Pavement Management and Rehabilitation
… applies a Lasso-based regularization scheme [Basis Pursuit coupled with Reweighted L1 Minimization] to simultaneously remove the white noise from the TSD deflection measurements and extract the deflection response generated as the TSD travels over the pavement's transverse joints. The examples …
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A spin on compressive sensing imaging : reticle-based single-pixel imaging system
… matrices showed higher recovery quality using basis pursuit recovery algorithms. It was observed that the restrictions posed on the binary sensing matrix using an edge-coded reticle did not limit the resulting sensing matrix recovery ability and even improved the capability compared to the …