Matlab code for Non-negative Matrix Factorization combining random projections and Nesterov iterations

Matthieu PUIGT c0ce29124c Ajouter 'Code/ReadMe.m' 6 лет назад
Code c0ce29124c Ajouter 'Code/ReadMe.m' 6 лет назад
Demo.m 0cf0905a24 Transférer les fichiers vers '' 6 лет назад
README.m 0cf0905a24 Transférer les fichiers vers '' 6 лет назад
README.md 0c3b9e6c34 Mettre à jour 'README.md' 6 лет назад
RSI_NeNMF.m f62a3ff3d6 Transférer les fichiers vers '' 6 лет назад
VANILLA_NeNMF.m f62a3ff3d6 Transférer les fichiers vers '' 6 лет назад
add_paths.m 0cf0905a24 Transférer les fichiers vers '' 6 лет назад
plots.m 0cf0905a24 Transférer les fichiers vers '' 6 лет назад
stop_rule.m 0cf0905a24 Transférer les fichiers vers '' 6 лет назад

README.m

% This is a matlab code for Non-negative Matrix Factorization via Nesterov's
% Optimal Gradient Method.

% In this project we have applied random projections to the aforementioned
% technique via two variants of random projections,namely:

% 1. Randomized Power iterations RPI NeNMF
% 2. Randomized Subspace Iterations RSI NeNMF

% HOW TO RUN THE CODE.
%
% Step 1: run the file "add_paths", to ensure all the folders and files are added to your matlab working path.
% Step 2: run the file "data_simulations", to generate your synthetic data.
% step 3: run the file "Demo" to execute the functions available in the folder "algorithms"

% Results of the execution are saved in the folder "output"

% To make plots, use the file "plots".