[The blog] [Compressed Sensing: The Big picture] [Local CS codes] [List of entries in the blog] 

Most of the material listed is a little outdated.I have used only about the first 100 entries on CS. As of Today, the current number of entries has gone beyond 220. For a better description of the on-going developement in compressive sensing, I have set up a living document on Compressed Sensing that can be found here. Also, since the current layout of Blogger does not allow much summarizing of the entries and since Compressed Sensing is such a dynamic framework, I have found it useful to try to parse of these entries on Compressed Sensing in the following list (the summary of all these entries is in Compressed Sensing: The Big Picture).

~ Compressed Sensing

• 1. Tutorials
  • 1.1 Videos
    • ~ Compressed Sensing Videos: Where Is My Popcorn ? Summer School IMA 2007
    • ~ Compressed Sensing video presentations
  • 1.2 Presentations
    • Heavy hitters/sketching
      • ~ Compressed Sensing: Streaming School in Denmark Summer 2007
  • 1.3 Examples
    • ~ Compressed Sensing: How to wow your friends.
  • ~ Compressed Sensing: The Framework
• 2. Short Reviews

  • ~ CS is not just Compressed Sampling nor Compressed Sensing.

    ~ Smashed filters and Bayesian approach to Compressed Sensing among others

    ~ Compressed Sensing: High Resolution Radar, Identification of Sparse Matrices

    ~ Compressed Sensing: Toeplitz, Radar, SAR, Bayesian Approach, Non Convex Minimization, Angle Preserving RIP

    ~ Compressed Sensing: Extending Reed-Solomon codes.

    ~ Compressed Sensing: stopping criterion, random multiscale projections, manifold learning, sparse image reconstruction

    ~ Compressed Sensing: Sensor Networks, Learning Compressed Sensing by Uncertain Compoent Analysis, Sparsity or Positivity ?, New CVX build

    ~ Compressed Sensing: Another deterministic code, a LASSO-CS root finder and a tree based greedy algorithm

    ~ Compressed Sensing: Neighborly polytopes, Acoustic Tomography, Sampling in union of linear subspaces, ROMP stability

    ~ Compressed Sensing: Sensor Networks, Learning Compressed Sensing by Uncertain Component Analysis, Sparsity or Positivity ?, New CVX build

    ~ Compressed Sensing: A Method for Large-Scale l1-Regularized Least Squares

    ~ Compressed Sensing: Sparse Measurement Matrices

    ~ Compressed Sensing: Near-ideal model selection by L1 minimization, Machine Learning: Sparse coding, Search and Rescue: InternetSAR

    ~ Compressed Sensing: Fast Compressive Sampling with Structurally Random Matrices

    ~ Compressed Sensing: Intrinsic Dimensionality of a Dataset, StOMP for large scale problems and 2007 WD5 is aiming at Mars

    ~ Compressed Sensing: Recovery Algorithms for Vector Valued Data, Wavelet-tailored Algorithm and a Network API for Sensor Networks

    ~ Compressed Sensing: Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors

    ~ Compressed Sensing: Greedier and Faster for Linear and Nonlinear Sparse Inverse Problems, A new version of Sparsify.

    ~ Compressed Sensing: Optimal non-linear models for sparsity and sampling

    ~ Compressed Sensing: Coded Aperture and CS in Astronomy, the Pareto frontier, Sampling bounds, Multiscale sparse image representation

    ~ Compressed Sensing: SPGL1 new version, Lp reconstructon from Random and Fourier Sampling, Random matrix norm, Randomness and Trees

• 3. Synthesis thoughts/ Theory

  • ~ Compressed Sensing: Random Thought on a Low Dimensional Embedding

    ~ Compressed Sensing: Reweighted L1 meets Europa

    On L1

    • ~ L1 -- What is it good for ?
    • ~ The importance of L1
    • ~ Why L1 ?

    ~ Synthesis on Compressed Sensing and Dimensionality Reduction/ Imaging a Fractal Romanesco. July 17, 2007

    ~ It's not about the sensor, it's about the target

    ~ Compressed Sensing Illustrated

    ~ Why Compressed Sensing is important when detecting movement

    Random matrices

    • ~ Random Projection Lapack ?

    Greedy algorithms

    • ~ Compressed Sensing: It's not just L1 anymore, greed can get you there too.

    ~ Amazing discoveries.

    3.2 Historical perspective

    • ~ Nothing short of a revolution, part 3 : It does not need to be nonlinear
    • ~ Nothing short of a revolution. Part deux: Pursuing a dream
    • ~ Nothing short of a revolution. Part I: When a scam can kill you

    ~ Compressed Sensing: How could Hardware implementations become disruptive technologies ?

    ~ Compressed Sensing: Compressive sampling vs conventional imaging, the Birth of the Everlasting Dataset

    ~ Compressed Sensing: Would Hierarchical Compressed Sensing Make Sense ?

    ~ Compressed Sensing: Visual Geometric Arguments

• 4. Hardware
  • 4.1 Rice Single Pixel Camera
    • ~ How does the Rice one pixel camera work ?
    • ~ Compressed Sensing: Single-Pixel Imaging via Compressive Sampling, Stable Manifold Embedding
  • 4.2 My implementation
    • ~ Implementing Compressed Sensing in Applied Projects
    • Hyper-GeoCam
      • ~ Ready to launch
      • ~ HASP will launch today.
  • 4.3 Other Implementations

    • ~ Compressed Sensing Hardware Implementations

      ~ Compressed Sensing Hardware Implementations (part deux)

      ~ Compressed Sensing: Hardware Implementation, Part III, The explosive birth of coded aperture.

      ~ Compressed Sensing: Hardware Implementation, Part IV, Hyperspectral Coded Aperture, Connection between the CS camera and the primary visual cortex ?

      ~ Compressed Sensing: Single-Pixel Imaging via Compressive Sampling, Stable Manifold Embedding

      ~ Compressed Sensing: Learning Bases from Scratch, an Optical Heterodyning Camera and RandomTime Coded Imaging

      ~ Compressed Sensing: Hardware Implementations in Computational Imaging, Coded Apertures and Random Materials

      ~ Compressed Sensing: Thoughts on Coded Aperture and Compressed Sensing

      ~ Compressive Sensing: Hardware Implementation update and some newer developments

      ~ Compressed Sensing: Computational Optical Imaging and Spectroscopy videos, I need some more popcorn.

  • 4.4 Disruptive Technology
    • ~ Compressed Sensing: Why does Rice Play Texas or How is CS a disruptive technology ? Part I
• + - 5. Algorithms
  • 5.1 Reconstruction Algorithm
    • ~ Compressed Sensing: Reweighted L1 and a nice summary on Compressed Sampling
    • ~ Compressed Sensing: Reweighted L1 meets Europa
    • ~ Compressed Sensing: Reweighted Lp through Least square (L2), Forget Europa, let's shoot for Titan
    • ~ Compressed Sensing: SpaRSA and Image Registration using Random Projections
    • ~ Compressed Sensing: Solving the Basis Pursuit problem with a Bregman Iterative Algorithm
    • ~ Compressed Sensing: A new TwIST
  • 5.2 Measurement Algorithm
    • ~ Compressed Sensing: Algorithms for Deterministic Compressed Sensing
    • ~ Compressed Sensing: Random Undersampling in Geophysics and ROMP, a stable greedy algorithm
    • ~ Compressed Sensing: Sparse Measurement Matrices
    • ~ Compressed Sensing: Toward Deterministic Subsampling
  • 5.3 Software Implementation

    • ~ SPGL1: a solver for large-scale sparse reconstruction problems

      ~ Tree based pursuit

      ~ Sparsify, another reason you should not avoid Compressed Sensing

      ~ There are now three Compressed Sensing Reconstruction Codes available

      ~ l1_ls: Simple Matlab Solver for l1-regularized Least Squares Problems (compressed sensing)

      • ~ Compressed Sensing: A Method for Large-Scale l1-Regularized Least Squares

      Bayesian approach

      • ~ Bayesian Compressive Sensing and other items.

      ~ Compressed Sensing: Chaining Pursuit Code for dimension reduction in the L1 norm for sparse vectors

      ~ Compressed Sensing: Sparco helps you test all these spiffy reconstruction algorithms

      ~ Monday Morning Algorithm Part deux: Reweighted Lp for Non-convex Compressed Sensing Reconstruction

      ~ Compressed Sensing: Sparse PCA and a Compressed Sensing Search Engine

      ~ Compressed Sensing: Restricted Isometry Properties and Nonconvex Compressive Sensing

      ~ Compressed Sensing: SpaRSA and Image Registration using Random Projections

      ~ Compressed Sensing: Utilizing Sparsity in Compressed Sensing Reconstruction Algorithms

      ~ Compressed Sensing: Using sparse representations for missing data imputation in noise robust speech recognition

  • 5.4 Monday Morning Algorithm

    • ~ Monday Morning Algorithm Part 3: Compressed Sensing meets Machine Learning / Recognition via Sparse Representation Classification Algorithm

      ~ Monday Morning Algorithm 7: Diffusion Maps for Dimensionality Reduction and Manifold Parametrization.

      ~ Monday Morning Algorithm 8: An Example of Compressed Measurements Speeding Up Diffusion Maps

      ~ Monday Morning Algorithm 9: Implementation of Sparse Measurement Matrices for Compressive Sampling

  • 5.5 Finding Sparse Bases

    • ~ Compressed Sensing: Building an Overcomplete Basis to Make Your Signal Sparse: Texture Decomposition using NMF

• + - 6. Applications
  • 6.1 Brain / Cognition
    • ~ Compressed Sensing in the Primary Visual Cortex ?
    • ~ Compressed Sensing, Primary Visual Cortex, Dimensionality Reduction, Manifolds and Autism
    • ~ Compressed Sensing: Would Hierarchical Compressed Sensing Make Sense ?
    • ~ Compressed Sensing: Random Features for Large-Scale Kernel Machines
  • 6.2 Machine Learning
    • ~ Machine Learning and Compressed Sensing
    • ~ Compressed Sensing: RandomFaces prove that faces lie in a low dimensional manifold
    • ~ Compressed Sensing: Random Features for Large-Scale Kernel Machines
  • 6.3 Solving integral equations
    • ~ Finite Rate of Innovation is the reason why S_n works in Neutron Transport ?
    • ~ Compressive Sensing as a way to solve Integral Equations ?
    • ~ Compressive Sensing Radiation Detector
    • ~ Approaching a Random Radiation Detector
  • 6.4 SAR

    • ~ Adding Search and Rescue Capabilities (part I): Using Hyperspectral and Multispectral Cameras to Search for Non-Evading Targets

  • 6.5 Network
    • ~ Compressed sensing: CS for networked data, Diffusion wavelets and Analog CS measurements.
  • 6.6 PDE
    • ~ Compressed Sensing: Nonlinear Approximation Techniques Using L1 and a Workshop.
  • 6.7 Other

    • ~ Compressed Sensing: Universal Dimensionality Reduction, Pattern Matching and a Biology Inspired Faster Reconstruction Algorithm

      ~ Sparse Representations and High Dimensional Geometry: The upcoming success of randomized algorithms

      ~ Deep down, Making sense of it all one bit at a time

      ~ Compressed Sensing: On-Line Structural Damage Detection

      ~ Compressed Sensing: Can we do Source Separation with Compressed Measurements ?

      ~ Compressed Sensing: The slow realization that most events are sparse in EEGs and in Astronomy

• 7. Reference Material
  • ~ Rice Compressed Sensing Site Update.
  • ~ Compressed Sensing: Blog entries and website updates.
  • ~ Compressed Sensings: Updates from Rice, a course and a blog
  • ~ Compressed Sensing: What's New in the field on a Calendar
• 8. Miscelaneous

  • ~ Traduction de Compressed Sensing en Francais

    ~ Compressed Sensing: Map of entries from this blog

    ~ Monday Morning Algorithm Part 1: Fast Low Rank Approximation using Random Projections

    ~ Trace: What is it good for ? - How Compressed Sensing relates to Dimensionality Reduction

    ~ Sparsity: What is it good for ?

    ~ Compressed Sensing Thoughts, Diffusion maps methods for Brain Imaging and for Computer Generated Graphics

    ~ Compressed Sensing: Blog entries and website updates.

    ~ Compressed Sensing: MCA news and Sublinear Recovery of Sparse Wavelet Signals

    ~ Compressed Sensing, Compressive Sampling: A Customized Search Engine

    ~ Sparsity Based Blind Source Separation

    Calendar

    • Conferences
      • ~ Compressed Sensing:ICASSP 2007, Some papers and posters and the ICASSP 2008 Abstracts
      • ~ Compressed Sensing: Links, ITA'08 at UCSD and EUSIPCO 2008 in Lausanne
    • Seminar
      • ~ Compressed Sensing: On the Interplay Between Coding Theory and Compressed Sensing

    ~ The 1003rd post: Compressed Sensing and a Tile.

    ~ Compressed Sensing: Oopsie