FSVM_train_St.m

function [ model, L ] = FSVM_train_St(Ytrain, trainset, option, opts, rho)
% By using the St (the total scatter matrix), no updating St
% trainset: nxd
%

% parameter settings
epsilon = opts.epsilon;
threshold = opts.threshold;
maxiter = opts.maxiter;
alpha = opts.alpha;

% calculate the total scatter matrix
S = cov( trainset, 1 ); % equal to cov( trainset ) * (size(trainset,1)-1)/size(trainset,1);
[ U, E ] = eig( full(S) );
[ dummy, order ] = sort( diag(E), 'descend' );
U = U( : , order );
% semi-whiten process
dummy = dummy + epsilon;
Sigma = diag((dummy*rho).^(-alpha));
M =  U * Sigma * U';    %M=L'L
L = Sigma.^(alpha)*U';

stop = 1; iter = 1; t = 0.1; beta = 0.9;
% initialize the (w,b)
model = svmtrain( Ytrain, trainset, option);
w = calc_w(model);
rho_ = rho/norm(w);
w = normalizemeanstd(w);
while stop > threshold && iter <= maxiter
    % update M
    A = w * w' + epsilon * eye(size(w,1));
    gradient =  - 0.5 * inv(M) * A * inv(M) + rho_ * S;
    
    M_tmp = M - t * gradient;
    M_tmp = M_tmp + epsilon*eye(size(M_tmp,1));
    [U_tmp, E_tmp] = eig(M_tmp);
    [ dummy_M, order_M ] = sort( diag(E_tmp), 'descend' );
    U_tmp = U_tmp( : , order_M );
    
    Sigma_tmp = diag(dummy_M);
    U_tmp = U_tmp( : , [1:numel(dummy_M)] );
    M_update = U_tmp*Sigma_tmp*U_tmp';
    M = M_update;
    
    % update (w,b) with M and input Ztrain:kxn
    L = Sigma_tmp*U';
    Ztrain = trainset*L';
    model = svmtrain( Ytrain, Ztrain, option);
    v = calc_w(model);
    w = L' * v;
    rho_ = rho/norm(w);
    w = w./repmat(sqrt(sum(w.^2,1)),size(w,1),1);
    S = cov( Ztrain, 1 );
    
    % the objective 
    obj = 0.5*norm(w'*U_tmp*diag(dummy_M.^(-1))*U_tmp'*w);
    
    % the stop criteria
    if iter > 1
        stop =  obj_previous - obj;
    end
    
    t = beta * t;
    obj_previous = obj;
    iter = iter+1;
end
end