What does this research mean for the field?

SHSRS improves the accuracy of approximate nearest neighbour search for dense vector embeddings through a novel cluster label refinement procedure. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to improve nearest neighbour search in dense vector embeddings through a refined clustering algorithm.

February 24, 20260 citationsOpen Access

SHSRS: Sementic Hierarchical Search with refined subspace

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CHChandan S H

Key Points

The aim is to improve nearest neighbour search in dense vector embeddings through a refined clustering algorithm.
Developed SHSRS for approximate nearest neighbour search.
Implemented the IGAR procedure for cluster label refinement.
Used KMeans for initial vector assignments and iteratively reassigned vectors to maximize neighborhood agreement.
Improved clustering quality by reframing partitioning as a neighbour retention problem.
Achieved better alignment of vector embeddings by leveraging kNN graph neighbourhoods.

Abstract

We present SHSRS (Semantic Hierarchical Search with Refined Subspace), an approximate nearest neighbour (ANN) search system for dense vector embeddings. The central algorithmic contribution is IGAR (Iterative Graph-Aligned Reassignment), a cluster label refinement procedure that reframes partition quality as a neighbour retention problem rather than a centroid proximity problem. Starting from a KMeans initialisation, IGAR iteratively reassigns vectors to the cluster that maximises agreement with their kNN graph neighbourhood...

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Cite This Study

Chandan S H (Sun,) studied this question.

synapsesocial.com/papers/699d4028de8e28729cf653ae https://doi.org/https://doi.org/10.5281/zenodo.18733029

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