Clustering on Simulated dataset Sparse weights, k=n/4
1.5Mean Run Time (s)AMA
Evaluation Results
| Method | Links | |
|---|---|---|
| AMANumber of points (n)=100, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04 | 1.5 | |
| AMANumber of points (n)=200, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04 | 2.94 | |
| AMANumber of points (n)=300, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04 | 4.46 | |
| ADMMNumber of points (n)=100, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04 | 5.42 | |
| AMANumber of points (n)=400, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04 | 6.02 | |
| SubgradientNumber of points (n)=100, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04 | 6.52 | |
| AMANumber of points (n)=500, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04 | 7.44 | |
| ADMMNumber of points (n)=200, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04 | 30.93 | |
| SubgradientNumber of points (n)=200, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04 | 37.42 | |
| ADMMNumber of points (n)=300, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04 | 88.63 | |
| SubgradientNumber of points (n)=300, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04 | 161.68 | |
| ADMMNumber of points (n)=400, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04 | 192.54 | |
| SubgradientNumber of points (n)=500, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04 | 386.45 | |
| ADMMNumber of points (n)=500, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04 | 436.49 | |
| SubgradientNumber of points (n)=400, Norm type=l2 norm, Weight sparsity configuration=Sparse weights2013.04 | 437.32 |