The Nuit Blanche Monday Morning Algorithm Series

Updated list of Monday Morning Algorithms listed on the Nuit blanche Blog 

17. A Compressive Sensing Approach to the Linear Black Box problem. The code is here.

16. S-POCS, Using Non Convex Sparse Signal Space Instead of the Convex l1 Ball Improves POCS Recovery. The code is here. The code was created and implemented solely by Laurent Jacques. [Also in Compressed Sensing codes]

15. Building a Noiselet Basis and Measurement Matrix. The code is here.It was implemented by Laurent Duval. [Also in Compressed Sensing codes]

14. A Comparison of the Reconstruction Capability of CoSaMP, OMP, Subspace Pursuit and Reweighted Lp. The code is here. Version 2. The code was implemented by David Mary. [Also in Compressed Sensing codes ]

12. Boosted Subspace Pursuit using Re-weighted Lp for Compressive Sensing. The code is here

11. Fast Compressive Imaging using Scrambled Block Hadamard Ensemble. The code is here.[Also in Compressed Sensing codes

10. Subspace Pursuit for Compressive Sensing. The code is here. [Also in Compressed Sensing codes ]

9. Sparse Compressed Sensing Measurement Matrix construction. The code is here. [Also in Compressed Sensing codes ]

8. An Example of Compressed Measurements Speeding Up Diffusion Maps. The code is here.  Buzz Lightyear is here.

7. Diffusion Maps for Dimensionality Reduction and Manifold Parametrization. The code is here. Buzz Lightyear is here

6. The Fast Johnson-Lindenstrauss Transform. The code is here.

5. The 1-D Experimental Probabilistic Hypersurface. The code is here (it includes MMA5, EPH and the dummy function to be evaluated called fina.m) 

4. Improving Embeddings by Flexible Exploitation of Side Information. with Cable Kurwitz. The code is here.

3.  Compressed Sensing meets Machine Learning / Recognition via Sparse Representation Classification Algorithm, with Jort Gemmeke. The code is here. [Also in Compressed Sensing codes ]

2.  Reweighted Lp for Non-convex Compressed Sensing Reconstruction, the code is here. [Also in Compressed Sensing codes ]

1. Fast Low Rank Approximation using Random Projections, the code is here.

The disclaimer for using these codes can be found here.

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