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rice大学压缩感知的书,想系统性学习CS的可以看看这本书
下载地址:http://cnx.org/exports/f70b6ba0-b9f0-460f-8828-e8fc6179e65f@5.12.pdf/an-introduction-to-compressive-sensing-5.12.pdf
1 Introduction
1.1 Introduction to compressive sensing
2 Sparse and Compressible Signal Models
2.1 Introduction to vector spaces
2.2 Bases and frames
2.3 Sparse representations
2.4 Compressible signals
3 Sensing Matrices
3.1 Sensing matrix design
3.2 Null space conditions
3.3 The restricted isometry property
3.4 The RIP and the NSP
3.5 Matrices that satisfy the RIP
3.6 Coherence
4 Sparse Signal Recovery via l1 Minimization
4.1 Signal recovery via l1 minimization
4.2 Noise-free signal recovery
4.3 Signal recovery in noise
4.4 Instance-optimal guarantees revisited
4.5 The cross-polytope and phase transitions
5 Algorithms for Sparse Recovery
5.1 Sparse recovery algorithms
5.2 Convex optimization-based methods
5.3 Greedy algorithms
5.4 Combinatorial algorithms
5.5 Bayesian methods
6 Applications of Compressive Sensing
6.1 Linear regression and model selection
6.2 Sparse error correction
6.3 Group testing and data stream algorithms
6.4 Compressive medical imaging
6.5 Analog-to-information conversion
6.6 Single-pixel camera
6.7 Hyperspectral imaging
6.8 Compressive processing of manifold-modeled data
6.9 Inference using compressive measurements
6.10 Compressive sensor networks
6.11 Genomic sensing
7 Appendices
7.1 Sub-Gaussian random variables
7.2 Concentration of measure for sub-Gaussian random variables
7.3 Proof of the RIP for sub-Gaussian matrices
7.4 l1 minimization proof
An Introduction to Compressive Sensing
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原文地址:http://www.cnblogs.com/axlute/p/4523973.html