tsneCluster

src/MATLAB/+Cluster/SpectralCluster.m

@@ -51,7 +51,7 @@
 % ******************************************************************************/
 
 % distance matrix A into k clusters
-function idx= SpectralCluster(A,k)
+function [idx,Y]= SpectralCluster(A,k)
 
 NREP=50;
 if (1==k)

src/MATLAB/+Cluster/tsneCluster.m

+% andy cohen -- Dec 16 2021
+%
+% tsne spectral clustering
+function [idx,Y] = tsneCluster(distanceMatrix,K)
+
+if (1==K)
+    idx=ones(size(A,2),1);
+    return;
+end
+% # of reps for kmeans
+NREP=50;
+% we use the matlab tsne with custom distance
+% pass the distance matrix to our embedded function here
+global tsneGlobalDistanceMatrix
+tsneGlobalDistanceMatrix=distanceMatrix;
+xsne=repmat([1:size(distanceMatrix,1)]',1,K);
+Y=tsne(xsne,'distance',@tsneDistance,'numDimensions',K);
+4;
+idx=kmeans(Y,K,'emptyaction','singleton','replicates',NREP);
+
+
+function d=tsneDistance(X,Y)
+global tsneGlobalDistanceMatrix
+
+for i=1:size(Y,1)
+    d(i,1)=tsneGlobalDistanceMatrix(X(1),Y(i,1));
+end
+
+4;

src/MATLAB/apps/nist digits/goTSNE.m

+% goTSNE.m
+
+targetSize=100;
+tsneConfusion=[];
+spectralConfusion=[];
+NUMBER_OF_TRIALS=100;
+p=startParallel(128);
+parfor nTrial=1:NUMBER_OF_TRIALS
+    target=[0:9];
+    K=length(target);
+    cardinality=ceil(targetSize/length(target));
+
+    xDigits=getNISTdigits(target,cardinality);
+    dDigits=MNISTdistance(xDigits);
+    idxSpectral=Cluster.SpectralCluster(dDigits,10);
+    cx=updateConfusion(xDigits,{},K,0,idxSpectral);
+    spectralConfusion(:,:,nTrial)=cx{10};
+    tic()
+    [idxTSNE,Y]=Cluster.tsneCluster(dDigits,10);
+    cx=updateConfusion(xDigits,{},K,0,idxTSNE);
+    tsneConfusion(:,:,nTrial)=cx{10};   
+    toc()      
+    5;
+end
+
+
+    
+    
+    
+