lrslibrary - Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos

•    Matlab

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Low-Rank and Sparse tools for Background Modeling and Subtraction in Videos. The LRSLibrary provides a collection of low-rank and sparse decomposition algorithms in MATLAB. The library was designed for motion segmentation in videos, but it can be also used (or adapted) for other computer vision problems (for more information, please see this page). Currently the LRSLibrary offers more than 100 algorithms based on matrix and tensor methods. The LRSLibrary was tested successfully in several MATLAB versions (e.g. R2014, R2015, R2016, R2017, on both x86 and x64 versions). It requires minimum R2014b.

rpca matrix-factorization matrix-completion tensor-decomposition tensor matlab matrix subspace-tracking subspace-learning

mctc4bmi - Matrix and Tensor Completion for Background Model Initialization

•    Matlab

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Please refer to main.m and demo.m files.

matlab matrix-completion tensor-completion matrix tensors background-modeling

rsparse - Fast and accurate machine learning on sparse matrices - Factorization Machines, FTRL, Matrix factorizations

•    R

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rsparse is an R package for statistical learning on sparse data. Notably it implements many algorithms sparse matrix factorizations with a focus on applications for recommender systems. All of the algorithms benefit from OpenMP and most of them use BLAS. Package scales nicely to datasets with millions of rows and millions of columns.

collaborative-filtering alternating-least-squares recommender-system r matrix-factorization matrix-completion singular-value-decomposition truncated-svd ftrl follow-the-regularized-leader factorization-machines

GDLibrary - Matlab library for gradient descent algorithms: Version 1.0.1

•    Matlab

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The GDLibrary is a pure-Matlab library of a collection of unconstrained optimization algorithms. This solves an unconstrained minimization problem of the form, min f(x). Note that the SGDLibrary internally contains this GDLibrary.

optimization optimization-algorithms machine-learning machine-learning-algorithms big-data gradient-descent gradient logistic-regression newton linear-regression svm lasso matrix-completion rosenbrock-problem softmax-regression multinomial-regression statistical-learning classification

OLSTEC - OnLine Low-rank Subspace tracking by TEnsor CP Decomposition in Matlab: Version 1.0.1

•    Matlab

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OLSTEC is an online tensor subspace tracking algorithm based on the Canonical Polyadic decomposition (CP decomposition) (or PARAFAC or CANDECOMP decomposition) exploiting the recursive least squares (RLS). Run run_me_first for path configurations.

subspace-learning tensor-cp-decomposition tensor-decomposition matrix-factorization matrix-completion low-rank-factorization online-learning stochastic-gradient-descent gradient-descent-algorithm background-subtraction grasta grouse petrels cp-decomposition tensor

SparseGDLibrary - MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.2

•    Matlab

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The SparseGDLibrary is a pure-Matlab library of a collection of unconstrained optimization algorithms for sparse modeling. Run run_me_first for path configurations.

optimization optimization-algorithms machine-learning-algorithms machine-learning big-data gradient-descent sparse-linear-solver sparse-regression lasso-regression lasso elasticnet solver algorithms admm proximal-algorithms proximal-operators logistic-regression matrix-completion coordinate-descent support-vector-machines