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Advanced Sparsity-Driven Models and Methods for Radar Applications

This book introduces advanced sparsity-driven models and methods and their applications in radar tasks such as detection, imaging and;

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Optimization with Sparsity-Inducing Penalties

linear variable selection but numerous extensions have now emerged such as structured sparsity or kernel selection. It turns out that many of the;

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Sparse Modeling

interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing. Sparse Modeling;

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Sparse Modeling

interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing. Sparse;

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Statistical Learning with Sparsity

or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and;

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Statistical Learning With Sparsity

weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and;

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Molecular Imaging in Nano MRI

, to consider the reconstruction of sparse images. Many works have leveraged the assumption of sparsity in order to achieve an improved;

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Sparsity Based Multipath Exploitation for Through the Wall Radar Imaging

This thesis reports on sparsity-based multipath exploitation methods for through-the-wall radar imaging. Multipath creates ambiguities;

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Compressed Sensing for MRI

measurements, signal sparsity, and a nonlinearsparsity promoting reconstruction. To optimize the performance of CS, the acquisition, thesparsifying;

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Compressed Sensing for Engineers

Makes subject matter accessible for engineers of various levels Focuses on algorithms including group-sparsity and row-sparsity, as well as;

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Waveform Diversity

sparsity-based receivers. Proven methods for mitigating noise and clutter and maximizing output signal power are included in this practical guide;

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Sparsity

This is the first book devoted to the systematic study of sparse graphs and sparse finite structures. Although the notion of sparsity;

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Compressed Sensing for Engineers

Makes subject matter accessible for engineers of various levels Focuses on algorithms including group-sparsity and row-sparsity, as well as;

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Semi-Supervised Learning and Domain Adaptation in Natural Language Processing

sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily;

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Sparse Optimization Theory and Methods

as the mainstream numerical algorithms for the sparsity-seeking problems arising from big-data processing, compressed sensing, statistical;

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Sparse Optimization Theory and Methods

as the mainstream numerical algorithms for the sparsity-seeking problems arising from big-data processing, compressed sensing, statistical;

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Regularized Optimization Methods for Reconstruction and Modeling in Computer Graphics

is insufficient for accurate reconstruction. As real-world examples of reconstruction problems, sparsity and group sparsity methods are presented for;

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Compressive Sensing for the Photonic Mixer Device

specific case of the Photonic Mixer Device (PMD).To this end, methods are presented that take profit of the sparsity of the signals gathered by PMD;

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Latent Variable Analysis and Signal Separation

analysis in observation sciences; ICA theory and applications; and sparsity-aware signal processing.;

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Sparse Estimation with Math and R

the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To;

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Sparse Estimation with Math and Python

introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To;

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Time-Frequency Analysis of Operators

elementary approach, along with applications to almost diagonalization of such operators and to the sparsity of their Gabor representations. Moreover;

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Learning Approaches in Signal Processing

forests, sparsity, and low-rank matrix estimation and cutting-edge visual/signal processing techniques, including face recognition, Kalman;

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Introduction To Sparse Stochastic Processes

Providing a novel approach to sparsity, this comprehensive book presents the theory of stochastic processes that are ruled by linear;

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Variational Bayesian Learning Theory

, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes;

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Mathematical Theories of Machine Learning - Theory and Applications

authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a;

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Sparse Image and Signal Processing

. Recent concepts of sparsity and morphological diversity are described and exploited for various problems such as denoising, inverse problem;

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