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Compressed Sensing and its Applications

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Cover of 'Compressed Sensing and its Applications'

Table of Contents

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    Book Overview
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    Chapter 1 On the Global-Local Dichotomy in Sparsity Modeling
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    Chapter 2 Fourier Phase Retrieval: Uniqueness and Algorithms
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    Chapter 3 Compressed Sensing Approaches for Polynomial Approximation of High-Dimensional Functions
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    Chapter 4 Multisection in the Stochastic Block Model Using Semidefinite Programming
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    Chapter 5 Recovering Signals with Unknown Sparsity in Multiple Dictionaries
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    Chapter 6 Compressive Classification and the Rare Eclipse Problem
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    Chapter 7 Weak Phase Retrieval
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    Chapter 8 Cubatures on Grassmannians: moments, dimension reduction, and related topics
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    Chapter 9 A Randomized Tensor Train Singular Value Decomposition
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    Chapter 10 Versatile and Scalable Cosparse Methods for Physics-Driven Inverse Problems
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    Chapter 11 Total Variation Minimization in Compressed Sensing
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    Chapter 12 Compressed Sensing in Hilbert Spaces
Attention for Chapter 6: Compressive Classification and the Rare Eclipse Problem
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