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A comment on E-means: 10x faster k-means at equal loss. Parent comment

Re:E-means: 10x faster k-means at equal loss by swann_bessa on Tuesday November 10, 2026 @10:50AM (#50442082)
BBD-tree is only for low dimensions because it suffers from the curse of dimensionality.

Note that BBD-tree is orthogonal to e-means, they can combined very straightforwardly just like it is combined with k-means. The 10x speedup should remain compared to BBD-tree k-means.

So it's not that e-means is better than BBD-tree, it's that you can combine them to make e-means even better, but only at low dimensions and on CPU.

[ Parent | on HN ]
Re:E-means: 10x faster k-means at equal loss by haifeng on Tuesday November 10, 2026 @12:50PM (#50443664)
Look deeper, I am not sure that we can combine BBD-tree with E-Means. BBD-tree is a data structure built on the whole dataset, while the core idea in E-Means is mini-batch with exponential moving average updates.
[ Parent | on HN ]