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ResearchFormerly WizWand

Clustering on Simulated dataset Sparse weights, k=n/4

1.5Mean Run Time (s)AMA
-15.9328101.7386219.41337.0814Apr 1, 2013
Updated 5mo ago

Evaluation results

MethodLinks
AMANumber of points (n)=100, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.041.5
AMANumber of points (n)=200, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.042.94
AMANumber of points (n)=300, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.044.46
ADMMNumber of points (n)=100, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.045.42
AMANumber of points (n)=400, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.046.02
SubgradientNumber of points (n)=100, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.046.52
AMANumber of points (n)=500, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.047.44
ADMMNumber of points (n)=200, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.0430.93
SubgradientNumber of points (n)=200, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.0437.42
ADMMNumber of points (n)=300, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.0488.63
SubgradientNumber of points (n)=300, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04161.68
ADMMNumber of points (n)=400, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04192.54
SubgradientNumber of points (n)=500, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04386.45
ADMMNumber of points (n)=500, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04436.49
SubgradientNumber of points (n)=400, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04437.32