e-means：更快的 k-means，现已纳入 FAISS 1.16.0

2026 年 11 月 11 日的一则公开帖文指向 e-means，一种现已纳入 FAISS v1.16.0 的聚类算法。帖文提到更快的检索与人工智能回答、更低的内存和显存占用、现已可用，以及可在标准服务器和本地硬件上运行，无需昂贵升级。

下文数字来自独立的项目页面和发布帖。摘录按原文引用。

“I’m excited to open-source e-means: a modern, robust k-means algorithm developed at Meta FAIR. A replacement of Lloyd's algorithm, e-means delivers a ~10× speedup in both epochs and wall-clock time while achieving lower loss.”

摘自 e-means 项目页开头，e-means.github.io。

“Across a large range of scales and datasets, e-means reaches a test loss below Lloyd's at 25 epochs, in 1.2 to 3 epochs. Measured end to end, that is a 10–45× wall-clock reduction on GPU against the state-of-the-art Lloyd implementation (Flash-KMeans [2]), and 8–15× on CPU against Faiss [1]. Speed and quality usually trade against each other in k-means, while e-means achieves both better speed and loss.”

摘自 e-means.github.io 上的 “An order of magnitude less epochs” 一节。

“Introducing e-means, an algorithm beating k-means in 10x fewer epochs. With order of magnitude wall-clock speedups, e-means is released in FAISS and needs no hyperparameter tuning beyond k. It is robust to various scales and datasets.”

摘自 Swann Bessa 于 2026 年 11 月 10 日发布的公告帖。

Swann Bessa 等，e-means: outperforming k-means (in one-tenth the epochs)。项目页：e-means.github.io。论文：arXiv:2611.09501。

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