sparse-plex
  • 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
  • File an issue
    • sparse-plex
    • Docs »
    • Compressive Sensing

    Compressive SensingΒΆ

    • 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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