annotate DL/Majorization Minimization DL/dict_update_REG_fn.m @ 155:b14209313ba4 ivand_dev

Integration of Majorization Minimisation Dictionary Learning

function [Phiout,unhatnz] = dict_update_REG_fn(Phi,x,unhat,maxIT,eps,cvset)
%% Regularized Dictionary Learning with the constraint on the matrix frobenius norms %%%%%
% Phi = Normalized Initial Dictionary
% x = Signal(x). This can be a vector or a matrix
% unhat = Initial guess for the coefficients
% to = 1/(step size) . It is larger than spectral norm of coefficient matrix x
% eps = Stopping criterion for iterative softthresholding and MM dictionary update
% cvset = Dictionary constraint. 0 = Non convex ||D|| = N, 1 = Convex ||D||<=N
% Phiout = Updated Dictionary
% unhatnz Updated Coefficients (the same as input in this version)

%%
B = Phi;
phim = norm(Phi, 'fro');
K = zeros(size(Phi,1),size(Phi,2));
c = .1 + svds(unhat,1)^2; 

%%
i = 1;       
while (sum(sum((B-K).^2)) > eps)&&(i<=maxIT)
    if i>1
        B = K;
    end
    K = 1/c *(x*unhat' + B*(c*eye(size(B,2))-unhat*unhat'));
    Kfn = sum(sum(K.^2));
    if cvset == 1,
        K = min(1,phim/Kfn)*K; % with convex constraint set
    else
        K = (phim/Kfn)*K; % with fixed-norm constraint set
    end
    i = i+1;
end

%% depleted atoms cancellation %%%
[Y,I] = sort(sum(K.^2),'descend');
RR = sum(Y>=0.0001);
Phiout = K(:,I(1:RR));
unhatnz = unhat(I(1:RR),:);
end