# Learning Cluster Representatives for Approximate Nearest Neighbor Search

arXiv: https://arxiv.org/abs/2412.05921 · HTML: https://mlsanity.com/paper/2412.05921/

## Key facts

- Cited by 1 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 8 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [Neighborhood Stability as a Measure of Nearest Neighbor Searchability](https://mlsanity.com/paper/2602.16673/) (2026)

## Abstract (arXiv)

Developing increasingly efficient and accurate algorithms for approximate nearest neighbor search is a paramount goal in modern information retrieval. A primary approach to addressing this question is clustering, which involves partitioning the dataset into distinct groups, with each group characterized by a representative data point. By this method, retrieving the top-k data points for a query requires identifying the most relevant clusters based on their representatives -- a routing step -- and then conducting a nearest neighbor search within these clusters only, drastically reducing the search space. The objective of this thesis is not only to provide a comprehensive explanation of clustering-based approximate nearest neighbor search but also to introduce and delve into every aspect of our novel state-of-the-art method, which originated from a natural observation: The routing function solves a ranking problem, making the function amenable to learning-to-rank. The development of this intuition and applying it to maximum inner product search has led us to demonstrate that learning cluster representatives using a simple linear function significantly boosts the accuracy of clustering-based approximate nearest neighbor search.