function [ T , RMSE ] = remnenmf_full( W , X , G , F , Omega_G, Omega_F, Phi_G, Phi_F , InnerMinIter , InnerMaxIter , Tmax , v, F_theo, delta_measure, nu) r = nu; X0 = X; Omega_G = (Omega_G == 1); % Logical mask is faster than indexing in matlab. Omega_F = (Omega_F == 1); % Logical mask is faster than indexing in matlab. nOmega_G = ~Omega_G; % Logical mask is faster than indexing in matlab. nOmega_F = ~Omega_F; % Logical mask is faster than indexing in matlab. [~, num_sensor] = size(F); num_sensor = num_sensor-1; em_iter_max = round(Tmax / delta_measure) ; T = nan(1,em_iter_max); RMSE = nan(2,em_iter_max); nW = (1-W); % X = G*F+W.*(X0-G*F); X = X0 + nW.*(G*F); GG = G' * G; GX = G' * X ; GradF = GG * F - GX; FF = F * F'; XF = X * F' ; GradG = nOmega_G.*(G * FF - XF); d = Grad_P([GradG',GradF],[G',F]); StoppingCritF = 1.e-3*d; StoppingCritG = 1.e-3*d; T_E = []; T_M = []; tic i = 1; niter = 0; RMSE(:,i) = vecnorm(F(:,1:end-1)- F_theo(:,1:end-1),2,2)/sqrt(num_sensor); T(i) = toc; while toc1 % [L,R]=RSI_compression(X,r,L,R); % else % [L,R]=RSI_compression(X,r); % end [L,R]=RSI_compression(X,r); % Compress left and right X_L = L * X; X_R = X * R; T_E = cat(1,T_E,toc - t_e); % Maximization step for j =1:v t_m = toc; % F_R = F * R; % FF = F_R * F_R'; FF = F * F'; XF = X_R * (F * R)' - Phi_G * FF; % G(Omega_G) = 0; % Convert G to \Delta G [ GradG , iterG ] = MaJ_G_EM_NeNMF( FF , XF , GradG , InnerMinIter , InnerMaxIter , StoppingCritG , nOmega_G); % Update \Delta G % G(Omega_G) = Phi_G(Omega_G); % Convert \Delta G to G G = GradG + Phi_G; niter = niter + iterG; if(iterG<=InnerMinIter) StoppingCritG = 1.e-1*StoppingCritG; end % G_L = L * G; % GG = G_L' * G_L; GG = G' * G; GX = (L * G)' * X_L - GG * Phi_F; F(Omega_F) = 0; % Convert F to \Delta F % F = F - Phi_F; [ F , iterF ] = MaJ_F_EM_NeNMF( GG , GX , F , InnerMinIter , InnerMaxIter , StoppingCritF , nOmega_F); % Update \Delta F F(Omega_F) = Phi_F(Omega_F); % Convert \Delta F to F % F = F + Phi_F; niter = niter + iterF; if(iterF<=InnerMinIter) StoppingCritF = 1.e-1*StoppingCritF; end if toc - i*delta_measure >= delta_measure i = i+1; if i > em_iter_max break end T(i) = toc; RMSE(:,i) = vecnorm(F(:,1:end-1) - F_theo(:,1:end-1),2,2)/sqrt(num_sensor); end T_M = cat(1,T_M,toc - t_m); end end niter disp(['rem E step : ',num2str(median(T_E))]) disp(['rem M step : ',num2str(median(T_M))]) end function [ L,R ] = RSI_compression(X,r,varargin) % Tepper, M., & Sapiro, G. (2016). Compressed nonnegative % matrix factorization is fast and accurate. IEEE Transactions % on Signal Processing, 64(9), 2269-2283. % see: https://arxiv.org/pdf/1505.04650 % The corresponding code is originally created by the authors % Then, it is modified by F. Yahaya. % Date: 13/04/2018 % compressionLevel=2; [m,n]=size(X); l = min(min(n,m), max(compressionLevel, r )); switch nargin case 2 OmegaL = randn(n,l); OmegaR = randn(l, m); q = 4; case 4 OmegaL = varargin{2}; OmegaR = varargin{1}; q = 1; end Y = X * OmegaL; for i=1:q [Y,~]=qr(Y,0); S=X'*Y; [Z,~]=qr(S,0); Y=X* Z; end [L,~]=qr(Y,0); L=L'; Y = OmegaR * X; for i=1:q [Y,~]=qr(Y',0); S=X*Y; [Z,~]=qr(S,0); Y=Z'*X; end Y=Y'; [R,~] = qr(Y,0); end