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Re:E-means: 10x faster k-means at equal loss by swann_bessa on Tuesday November 10, 2026 @10:05AM (#50441420)
Careful tuning is provided by the scaling laws, so you don't need to set the hyperparameters yourself. I am quite skeptical about setting alpha=-0.1 tbh, but I'm curious to see what happens too lol.
Re:E-means: 10x faster k-means at equal loss by shiandow on Tuesday November 10, 2026 @12:03PM (#50443100)
It's prpbably going to diverge, but in this case there's not really a dataset to begin with. It's just alternating calculating the centroids with calculating the Voronoi diagrams. The reason it's slow is not because it can't escape a local minimum it's because the two steps are only slowly converging on the optimum.

So something that ends up increasing the step size might actually help. But in practice you're probably better of doing something more orthodox like conjugate gradient or Newton's.