Add SpectralClustering.

utils/SpectralClustering.m

@@ -0,0 +1,30 @@
+%--------------------------------------------------------------------------
+% This function takes an adjacency matrix of a graph and computes the 
+% clustering of the nodes using the spectral clustering algorithm of 
+% Ng, Jordan and Weiss.
+% CMat: NxN adjacency matrix
+% n: number of groups for clustering
+% groups: N-dimensional vector containing the memberships of the N points 
+% to the n groups obtained by spectral clustering
+%--------------------------------------------------------------------------
+% Copyright @ Ehsan Elhamifar, 2012
+%--------------------------------------------------------------------------
+
+function groups = SpectralClustering(CKSym,n)
+
+    warning off;
+    N = size(CKSym,1);
+    MAXiter = 1000; % Maximum number of iterations for KMeans 
+    REPlic = 100; % Number of replications for KMeans
+
+    % Normalized spectral clustering according to Ng & Jordan & Weiss
+    % using Normalized Symmetric Laplacian L = I - D^{-1/2} W D^{-1/2}
+
+    DN = diag( 1./sqrt(sum(CKSym)+eps) );
+    LapN = speye(N) - DN * CKSym * DN;
+    [uN,sN,vN] = svd(LapN);
+    kerN = vN(:,N-n+1:N);
+    for i = 1:N
+        kerNS(i,:) = kerN(i,:) ./ norm(kerN(i,:)+eps);
+    end
+    groups = kmeans(kerNS,n,'maxiter',MAXiter,'replicates',REPlic,'EmptyAction','singleton');