title: Errors out of memory running Matlab Autoencoders on 10^5 sparse matrix
body: <p>I have a 10^5 square sparse matrix called pbAttack. Each element represents if there is connection between node i and node j. If there is connection, pbAttack(i,j) = 1. Otherwise, pbAttack (i,j) = 0. Then I want to use it following this tutorial: <a href="http://www.mathworks.com/help/nnet/ug/construct-deep-network-using-autoencoders.html" rel="nofollow" title="Matlab AutoEncoders">Matlab Autoencoders</a>. I use the same code as in the linked Matlab tutorial. I only change it to use my data.
  However, I got following errors:</p>
  
  <pre><code>Error using bsxfun
  Out of memory. Type HELP MEMORY for your options.
  
  Error in mapminmax.apply (line 8)
  y = bsxfun(@plus,y,settings.ymin);
  
  Error in mapminmax.create (line 44)
   y = mapminmax.apply(x,settings);
  
  Error in mapminmax (line 51)
  [y,settings] = mapminmax.create(x,param);
  
  Error in nnet.internal.configure.input (line 31)
  [x,config] = feval(processFcns{j},x,processParams{j});
  
  Error in network/configure (line 234)
  net = nnet.internal.configure.input(net,i,X{i});
  
  Error in nntraining.config (line 116)
  net = configure(network(net),X,T);
  
  Error in nntraining.setup&gt;setupPerWorker (line 68)
  [net,X,Xi,Ai,T,EW,Q,TS,err] =   nntraining.config(net,X,Xi,Ai,T,EW,configNetEnable);
  
  Error in nntraining.setup (line 43)
  [net,data,tr,err] = setupPerWorker(net,trainFcn,X,Xi,Ai,T,EW,enableConfigure);
  
  Error in network/train (line 335)
  [net,data,tr,err] =    nntraining.setup(net,net.trainFcn,X,Xi,Ai,T,EW,enableConfigure,isComposite);
  
  Error in Autoencoder.train (line 490)
          net = train(net,X,X,'useGPU',iYesOrNo(useGPU));
  
  Error in trainAutoencoder (line 105)
  autoenc = Autoencoder.train(X, autonet, paramsStruct.UseGPU);
  
  Error in workflow_autoencoder (line 8)
  autoenc1 = trainAutoencoder(pbAttack,hiddenSize,...
  </code></pre>
  
  <p>Are all these errors caused by the huge size of the matrix? Is there a work around so that it does not need so much memory? Thank you so much.</p>