Research map / Learning theory and algorithms / Clustering
Unified Locally Linear Classifiers With Diversity-Promoting Anchor Points
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add diversified regularization (U-LLSVM -> DU-LLSVM) (ablation) | U-LLSVM · phishing | accuracy (%): 95.66 -> 95.84 | Helped |
| add diversified regularization (U-LLSVM -> DU-LLSVM) (ablation) | U-LLSVM · Magic04 | accuracy (%): 84.88 -> 85.14 | Helped |
| add diversified regularization (U-LLSVM -> DU-LLSVM) (ablation) | U-LLSVM · IJCNN | accuracy (%): 97.66 -> 98.26 | Helped |
| add diversified regularization (U-LLSVM -> DU-LLSVM) (ablation) | U-LLSVM · w8a | accuracy (%): 98.74 -> 98.96 | Helped |
| add diversified regularization (U-LLSVM -> DU-LLSVM) (ablation) | U-LLSVM · connect-4 | accuracy (%): 89.15 -> 90.94 | Helped |
| add diversified regularization (U-LLSVM -> DU-LLSVM) (ablation) | U-LLSVM · Covtype | accuracy (%): 81.75 -> 81.99 | Helped |
| jointly optimize anchor points, coding and classifier (SAPL-LLSVM -> U-LLSVM) | SAPL-LLSVM · Magic04 | accuracy (%): 81.60 -> 84.88 | Helped |
| jointly optimize anchor points, coding and classifier (SAPL-LLSVM -> U-LLSVM) | SAPL-LLSVM · w8a | accuracy (%): 98.74 -> 98.74 | No clear effect |
| learn anchor points instead of k-means (LLSVM -> SAPL-LLSVM) | LLSVM · phishing | accuracy (%): 92.66 -> 93.92 | Helped |
| add local soft-assignment coding (SVM -> LLSVM) | SVM · Covtype | accuracy (%): 68.73 -> 79.17 | Helped |
| increase number of anchor points from 10 to 100 | U-LLSVM and DU-LLSVM · hinge loss vs anchor points | test error | Helped |
| use diversified regularization with fewer anchor points | DU-LLSVM · hinge loss vs anchor points | test error | Helped |
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