Compressive Sensing Listing /

Compressed Sensing Listing

An incomplete summary of the recent papers / links / blog posts listed on Nuit Blanche on the subject of compressive sensing or compressed sensing. 

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  1. Iterative Hard Thresholding for Compressed Sensing, also at arXiv.
  2. Stagewise Weak Gradient Pursuits. Part I: Fundamentals and Numerical Studies.
  3. Stagewise Weak Gradient Pursuits. Part II: Theoretical Properties.
  1. Compressive sensing and signal subspace methods for inverse scattering including multiple scattering
  2. Subspace and Bayesian compressive sensing methods in imaging
  3. Inverse scattering by compressive sensing and signal subspace methods
  1. Planar multi-flows, L_1 embeddings, and differentiation
  2. The pseudorandom subspace problem
  1. Lecture 1: Background, Techniques, Methods
  2. Lecture 2: Concentration of Measure
  3. Lecture 3: Concentration of Measure (cont'd)
  4. Lecture 4: Dimension Reduction
  5. Lecture 5: Subgaussian Random Variables
  6. Lecture 6: Norm of a Random Matrix
  7. Lecture 7: Largest, Smallest, Singular Values of Random Rectangular Matrices
  8. Lecture 8: Dudley's Integral Inequality
  9. Lecture 9: Applications of Dudley's Inequality - Sharper Bounds for Random Matrices
    Lecture 10:
    Slepian's Inequality - Sharpness Bounds for Gaussian Matrices
  10. Lecture 11: Gordon's Inequality
  11. Lecture 12: Sudakov's Minoration
  12. Lecture 13: Sections of Convex Sets via Entropy and Volume
  13. Lecture 14: Sections of Convex Sets via Entropy and Volume (cont'd)
  14. Lecture 15: Invertibility of Square Gaussian Matrices, Sparse Vectors
  15. Lecture 16: Invertibility of Gaussian Matrices and Compressible/Incompressible Vectors
  16. Lecture 17: Invertibility of Subgaussian Matrices - Small Ball Probability via the Central Limit Theorem
  17. Lecture 18: Strong Invertibility of Subgaussian Matrices and Small Ball Probability via Arithmetic Progression
  18. Lecture 19: Small Ball Probability via Sum-Sets
  19. Lecture 20: The Recurrence Set (Ergodic Approach)