Research map / Learning theory and algorithms / Clustering
Balanced Clustering via Exclusive Lasso: A Pragmatic Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add exclusive lasso balance constraint to k-means | k-means · MNIST clustering | accuracy (%): 52.6±3.3 -> 57.3±2.4 | Helped |
| add exclusive lasso balance constraint to k-means | k-means · Coil20 clustering | accuracy (%): 68.4±2.8 -> 73.1±2.3 | Helped |
| add exclusive lasso balance constraint to k-means | k-means · YaleB clustering | accuracy (%): 16.3±1.1 -> 43.5±1.8 | Helped |
| add exclusive lasso balance constraint to min-cut | Min-Cut · MNIST clustering | accuracy (%): 56.4±2.8 -> 61.4±2.0 | Helped |
| add exclusive lasso balance constraint to min-cut | Min-Cut · Coil20 clustering | accuracy (%): 70.3±2.4 -> 81.6±1.9 | Helped |
| add exclusive lasso balance constraint to min-cut | Min-Cut · UMIST clustering | NMI (%): 82.7±2.8 -> 94.8±2.9 | Helped |
| vary regularization weight gamma (ablation) | Balanced k-means and Balanced Min-Cut · nine datasets | accuracy | Mixed |
| add discriminative dimension reduction (DisCluster, DisKmeans) to k-means | k-means · nine datasets | accuracy | Helped |
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