Research map / Learning theory and algorithms / Clustering
On Convergence of Epanechnikov Mean Shift
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| When an iterate stalls on a bandwidth-boundary point, add one such boundary data point to the averaging update (ablation) | Epanechnikov Mean Shift · Estimated density optimization | termination at a local optimum | Helped |
| Use deflation to cluster points around each found mode rather than starting Mean Shift at every data point | Epanechnikov Mean Shift · Non-overlapping spherical Gaussian mixtures | computational cost | Helped |
| Use deflation instead of running the iterates at every data point | Epanechnikov Mean Shift · Synthetic Gaussian mixture, d=100, K=30 | clustering error | No clear effect |
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