Summer generally allows for some downtime at which point I can update other pages such as the Reproducible Research ( implementations ) page, the Big Picture in Compressive Sensing or the Advanced Matrix Factorization Jungle page. Here are the implementations I will add shortly to these pages. They include all implementations listed here on the Nuit Blanche in Review starting January 2014 till yesterday's entry:

Compressive Sensing

- Adaptive-Rate Compressive Sensing Using Side Information - implementation -
- NLCS : Non-Local Compressive Sampling Recovery - implementation -
- Multidimensional Compressed Sensing and their Applications - implementation -
- The SwAMP Thing! Sparse Estimation with the Swept Approximated Message-Passing Algorithm -implementation -
- On the Convergence of Approximate Message Passing with Arbitrary Matrices
- NLR-CS : Compressive Sensing via Nonlocal Low-rank Regularization - implementation -
- Matrix-free Interior Point Method for Compressed Sensing Problems / A Second order Method for Compressed Sensing with Coherent and Redundant Dictionaries - implementation -
- emd_flow : The Constrained Earth Mover Distance Model,with Applications to Compressive Sensing
- Tree_approx : A Fast Approximation Algorithm for Tree-Sparse Recovery
- CGIHT, ASD and ScaledASD: Compressed Sensing and Matrix Completion
- GAGA: GPU Accelerate Greedy Algorithms for Compressed Sensing - new version -
- RCoS : Image Compressive Sensing Recovery via Collaborative Sparsity
- GrAMPA: Generalized Approximate Message Passing for Analysis Compressive Sensing
- SparseFHT: A Fast Hadamard Transform for Signals with Sub-linear Sparsity in the Transform Domain - implementation -
- EMaC : Robust Spectral Compressed Sensing via Structured Matrix Completion
- Summary of Contribution: Compressed sensing with linear correlation between signal and measurement noise
- STSBL-EM : Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
- Sparse Spikes Deconvolution with Continuous Basis-Pursuit
- Bayesian Pursuit Algorithms
- Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization

Regression

Optimization solvers

Compressive Detection

Sparse Approximation/Reconstruction/Denoising

Blind Deconvolution

Matrix Factorization (other than NMF)

- GreBsmo: Greedy Bilateral Sketch, Completion & Smoothing - implementation -
- Tight convex relaxations for sparse matrix factorization - implementation -
- Fast matrix completion without the condition number - implementation -
- Dynamic MR image reconstruction–separation from undersampled (k,t)-space via low-rank plus sparse prior - implementation -
- TGA: Grassmann Averages for Scalable Robust PCA
- Spectral redemption: clustering sparse networks
- Sparse Randomized Kaczmarz for Multiple Measurement Vectors
- Fast and Robust Archetypal Analysis for Representation Learning
- Probabilistic Archetypal Analysis
- ROSL : Robust Orthonormal Subspace Learning: Efficient Recovery of Corrupted Low-rank Matrices - implementation -
- CGIHT, ASD and ScaledASD: Compressed Sensing and Matrix Completion
- PSPG: Efficient Algorithms for Robust and Stable Principal Component Pursuit Problems
- CPCP: Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods
- ADMIP: An Alternating Direction Method with Increasing Penalty for Stable Principal Component Pursuit
- OptShrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage
- Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices
- Introduction into cross approximation / Fast multidimensional convolution in low-rank formats via cross approximation - implementation -
- LRRSC : Subspace Clustering by Exploiting a Low-Rank Representation with a Symmetric Constraint - implementation -
- WNNM: Weighted Nuclear Norm Minimization with Application to Image Denoising
- Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
- Low-Rank Modeling of Local k-Space Neighborhoods (LORAKS): Implementation and Examples for Reproducible Research
- IRNN: Generalized Nonconvex Nonsmooth Low-Rank Minimization
- HASI: Probabilistic Low Rank Matrix Completion with Adaptive Spectral Regularization Algorithms
- Interest Zone Matrix Approximation

NMF

- Random Projections for Non-negative Matrix Factorization
- Enhancing Pure-Pixel Identification Performance via Preconditioning - implementation -

Nonlinear Compressive Sensing

One-Bit

Phase Retrieval

Randomized Numerical Linear Algebra

Tensor

Machine Learning

- Mondrian Forests: Efficient Online Random Forests - implementation -
- Understanding Random Forests: From Theory to Practice - implementation -
- SPAL: Sparse Projection-based Adaptive Learning
- Active Subspace: Towards Scalable Low-Rank Learning
- A Riemannian approach to low-rank algebraic Riccati equations - implementation -
- KNIFE: Automatic Feature Selection via Weighted Kernels and Regularization
- Avoiding pathologies in very deep networks
- Kernel LMS algorithm with forward-backward splitting for dictionary learning
- MISO: Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning

Subspace Learning

- LSR: Robust and Efficient Subspace Segmentation via Least Squares Regression
- PETRELS: Parallel Subspace Estimation and Tracking by Recursive Least Squares from Partial Observations

Applications

- Randomized Comments: Code for GWAS and CS study, a new blog, Geometric theory of information
- PURIFY: a new algorithmic framework for next-generation radio-interferometric imaging - implementation -
- REMODE: Probabilistic, Monocular Dense Reconstruction in Real Time
- HODLR and george: Fast Direct Methods for Gaussian Processes and the Analysis of NASA Kepler Mission Data - implementation -
- CaSPIAN: A Causal Compressive Sensing Algorithm for Discovering Directed Interactions in Gene Networks - implementation -
- Saturday Morning Video: SVO: Fast Semi-Direct Monocular Visual Odometry - implementation -
- SUGAR : Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection
- Selecting thresholding and shrinking parameters with generalized SURE for low rank matrix estimation
- The Flutter Shutter Code Calculator - implementation -

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