用低精度向量聚类，大幅降低存储和时间开销。
Stop Indexing at Full Precision: Revisiting Clustering for Vector Embeddings
- 先降维/量化再聚类，优化向量索引流程
- 1比特编码聚类质量仅比理想差1%，存储减少60倍
- 适合追求高效向量搜索的工程应用
本研究重新审视了向量搜索中三种常用技术：降维、量化与维度剪枝，并将其用于优化向量嵌入索引。我们提出一种在聚类前应用这些技术的索引流水线，重点分析其对存储开销、聚类时间和中心点质量的影响。结果表明，使用全精度向量进行聚类是冗余的；即使采用1比特编码，聚类质量仍可达到理想水平的99%以上，同时存储需求降低60倍，并带来显著性能提升（见图1）。相关实现已开源至https://github.com/cwida/SuperKMeans。
原文摘要 · Abstract (English)
In this study, we revisit three widely used techniques in vector search and utilize them to optimize vector embedding indexing through clustering: dimensionality reduction, quantization, and dimension pruning. We propose an indexing pipeline in which these techniques are applied before clustering, and we focus on how they affect storage footprint, clustering time, and the quality of the resulting centroids for vector search tasks. Our results reveal that using full-precision vectors for clustering is excessive, as even 1-bit codes can achieve near-optimal clustering quality (within 1% of ideal) while reducing storage requirements by 60x and delivering attractive performance gains (Figure 1). We open-source our implementations at https://github.com/cwida/SuperKMeans.
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