"out of memory" message for mvregress

I am trying to use mvregress with the data I have with dimensionality of a couple of hundreds. (3~4). Using 32 gb of ram, I can not compute beta and I get "out of memory" message. I couldn't find any limitation of use for mvregress that prevents me to apply it on vectors with this degree of dimensionality, am I doing something wrong? is there any way to use multivar linear regression via my data?

here is an example of what goes wrong:

dim=400;
nsamp=1000;
dataVariance = .10;
noiseVariance = .05;
mixtureCenters=randn(dim,1);
X=randn(dim, nsamp)*sqrt(dataVariance ) + repmat(mixtureCenters,1,nsamp);
N=randn(dim, nsamp)*sqrt(noiseVariance ) + repmat(mixtureCenters,1,nsamp);
A=2*eye(dim);

Y=A*X+N;

% A_hat=mvregress(X',Y');
[B, y_hat]=mlrtrain(X,Y)

where

function [B, y_hat]=mlrtrain(X,Y)
[n,d] = size(Y);
Xmat = [ones(n,1) X];
Xmat_sz=size(Xmat);
Xcell = cell(1,n);
for i = 1:n
    Xcell{i} = [kron([Xmat(i,:)],eye(d))];
end
[beta,sigma,E,V] = mvregress(Xcell,Y);
B = reshape(beta,d,Xmat_sz(2))';
y_hat=Xmat * B ;
end

[Jason Moore]

Make sure you have increased your Java Heap size. You can do this by doing the following

Click on the Home button in MATLAB -> Preferences -> General -> Java Heap

Try increasing this setting and restarting MATLAB

[Anoosh]

thank you @jasonMoore but I can see from my memory manager that matlab is using my 32 gb of ram plus a 32 gb of swap. In sum, it is using around 64 gb of ram and still is not able to solve this linear regression problem.