e-means: outperforming k-means in one-tenth the epochs
Summary
e-means introduces a mini-batch k-means algorithm that significantly speeds up training while reducing loss compared to Lloyd's approach. It uses exponential moving average updates, cosine scheduling, dead-cluster revival, and automatic scaling laws to set batch sizes and learning rates, with open-source Faiss implementations.