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About Sparse-Plex
Getting Started
Demos
Sparse Signal Models
Compressive Sensing
Introduction to compressive sensing
Recovery of exactly sparse signals
Recovery in presence of measurement noise
The RIP and the NSP
Matrices satisfying RIP
Subgaussian distributions
Rademacher sensing matrices
Gaussian sensing matrices
Examples
Data Analysis
Data Clustering
Pursuit Algorithms
Subspace Clustering
Dictionary Learning
Set Theory
Linear Algebra
Matrix Algebra
Real Analysis
Convex Analysis
Probability and Random Variables
Geometry
Numerical Optimization
Digital Signal Processing
Wavelets
Detection, Classification and Estimation
ECG
Computational Complexity
Library Classes
Exercises
Scripts
References
Index
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Compressive Sensing
Compressive Sensing
ΒΆ
Introduction to compressive sensing
The sensing matrix
Number of measurements
Signal recovery
Error correction in linear codes
Recovery of exactly sparse signals
The spark
Recovery of approximately sparse signals
Measuring the performance of a recovery algorithm
Recovery in presence of measurement noise
Restricted isometry property
Stability
Measurement bounds
The RIP and the NSP
Matrices satisfying RIP
Conditions on random distribution for RIP
Sub Gaussian random matrices satisfy the RIP
Advantages of random construction
Subgaussian distributions
Characterization of subgaussian random variables
More properties
Subgaussian random vectors
Rademacher sensing matrices
Joint correlation
Coherence of Rademacher sensing matrix
Gaussian sensing matrices
Joint correlation
Hands on with Gaussian sensing matrices
Examples
Piecewise cubic polynomial signal
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