Adaptive Prefiltering for High-Dimensional Similarity Search: A Frequency-Aware Approach

TL;DR

This paper introduces an adaptive prefiltering framework for high-dimensional similarity search that uses query frequency patterns and cluster coherence to optimize computational resources, improving efficiency without sacrificing recall.

Contribution

It proposes a novel frequency-aware adaptive prefiltering approach that dynamically allocates search budgets based on query distribution and local data density.

Findings

Achieves 20.4% fewer distance computations while maintaining recall.

Maintains sub-millisecond latency on GPU-accelerated FAISS indices.

Provides minimal overhead and graceful fallback for unseen queries.

Abstract

High-dimensional similarity search underpins modern retrieval systems, yet uniform search strategies fail to exploit the heterogeneous nature of real-world query distributions. We present an adaptive prefiltering framework that leverages query frequency patterns and cluster coherence metrics to dynamically allocate computational budgets. Our approach partitions the query space into frequency tiers following Zipfian distributions and assigns differentiated search policies based on historical access patterns and local density characteristics. Experiments on ImageNet-1k using CLIP embeddings demonstrate that frequency-aware budget allocation achieves equivalent recall with 20.4% fewer distance computations compared to static nprobe selection, while maintaining sub-millisecond latency on GPU-accelerated FAISS indices. The framework introduces minimal overhead through lightweight frequency tracking and provides graceful degradation for unseen queries through coherence-based fallback policies.