Fixes for the Laplacian Eigenmaps problem

+Cluster/LaplacianEigenmaps.m

 % Produce k-dimensional eigenvectors of symmetric Laplacian of A
 function v_pts = LaplacianEigenmaps(A,k)
-    D = zeros(size(A));
-    for i=1:size(A,1)
-        D(i,i)=sum(A(i,:));
-    end
+    d = sum(A,2);
+    
+    bValid = d > 0;
+    dInvSqrt = zeros(size(d));
+    dInvSqrt(bValid) = 1 ./ sqrt(d(bValid));
+    
+    D = diag(d);
+    Disqrt = diag(dInvSqrt);
 
     L = D - A;
-    L = D^(-.5) * L * D^(-.5);
+    L = Disqrt * L * Disqrt;
 
-    % Regularize the affinity matrix
-    for i=1:size(L,1)
-         for j= 1:size(L,2)
-             L(i,j)= max(L(i,j),L(j,i));
-         end
-         L(i,i)=0;
-    end
+%     % Regularize the affinity matrix
+%     for i=1:size(L,1)
+%          for j= 1:size(L,2)
+%              L(i,j)= min(L(i,j),L(j,i));
+%          end
+% %          L(i,i)=1;
+%     end
 
     [eVec,eVal] = eig(L);
     evals = diag(eVal);
 
     [evals,srtIdx] = sort(evals);
-    v_pts = eVec(:,srtIdx(2:k+1));
+    v_pts = eVec(:,srtIdx(2:(k+1)));
 end