Meta FAIR researchers have released e-means, a drop-in replacement for Lloyd’s k-means algorithm. The e-means clustering algorithm reaches the loss Lloyd’s k-means gets in 25 epochs in 1.4 to 2.6 epochs. It ships in FAISS 1.16.0 under faiss.contrib.e_means. For anyone building vector indexes, quantizers or data-curation pipelines, clustering is often the slowest offline step. e-means targets exactly that step.
e-means is a mini-batch k-means algorithm from Meta FAIR that updates centroids with exponential moving averages.
Every k-means variant keeps a sum S and a count N per cluster. The centroid is S divided by N. Methods differ only in how S and N change.
Older assignments fade, so centroids track recent data. The research team expa Lloyd is a corner case: full batch with α = 1. On DEEP, that corner sits 1.8% above the best loss.
Source: MarkTechPost — Read the full article on the original site ↗