Research map / Learning theory and algorithms / Clustering
Clustering Semi-Random Mixtures of Gaussians
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Lloyd's algorithm with the paper's initialization under monotone semi-random perturbations | Lloyd's algorithm for k-means · Semi-random GMM | misclassified points: at most \frac{c_1 k d}{\Delta^4}\cdot \max\Bigset{1,\log\parens[\big]{\tfrac{3(\sqrt{d}+2\sqrt{\log N})}{\Delta^2}}} | Helped |
| Allow monotone semi-random perturbations of spherical Gaussian samples | k-means clustering · Semi-random spherical GMM | misclassified points: at least \frac{c_1 k d}{\Delta^4} | Hurt |
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