用极简非线性降维加速向量搜索，兼顾精度与效率
GleanVec: Accelerating vector search with minimalist nonlinear dimensionality reduction
- 提出线性与非线性降维方法，降低高维向量维度
- GleanVec在跨模态检索中提升搜索精度，速度更快
- 适合需要快速精准向量检索的应用场景
嵌入模型生成的高维向量能反映语义相似性，因此在大规模集合中高效准确地检索与查询向量相似的项，已成为众多应用的关键。特别是跨模态检索（如用文本查询找图像）正迅速发展，但查询与数据库向量常存在统计分布差异，导致精度挑战。此外，高维向量带来计算与内存压力，影响系统性能。本文提出新的线性与非线性降维方法，以加速高维向量搜索，同时在分布内（ID）和分布外（OOD）查询设置下保持精度。线性方法LeanVec-Sphering优于其他线性方法，训练更快，无需超参数，且目标维度可灵活设定。非线性方法Generalized LeanVec（GleanVec）采用分段线性方案，进一步提升搜索精度，同时保持计算轻量。初步实验表明，LeanVec-Sphering与GleanVec达到了向量搜索的最新技术水平。
原文摘要 · Abstract (English)
Embedding models can generate high-dimensional vectors whose similarity reflects semantic affinities. Thus, accurately and timely retrieving those vectors in a large collection that are similar to a given query has become a critical component of a wide range of applications. In particular, cross-modal retrieval (e.g., where a text query is used to find images) is gaining momentum rapidly. Here, it is challenging to achieve high accuracy as the queries often have different statistical distributions than the database vectors. Moreover, the high vector dimensionality puts these search systems under compute and memory pressure, leading to subpar performance. In this work, we present new linear and nonlinear methods for dimensionality reduction to accelerate high-dimensional vector search while maintaining accuracy in settings with in-distribution (ID) and out-of-distribution (OOD) queries. The linear LeanVec-Sphering outperforms other linear methods, trains faster, comes with no hyperparameters, and allows to set the target dimensionality more flexibly. The nonlinear Generalized LeanVec (GleanVec) uses a piecewise linear scheme to further improve the search accuracy while remaining computationally nimble. Initial experimental results show that LeanVec-Sphering and GleanVec push the state of the art for vector search.
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