Stop Indexing at Full Precision: Compressed Vectors Cut Storage by 60x
_reachsumit · x · 2026-08-18
A VLDB 2026 paper proposes optimizing vector embedding indexing by applying dimensionality reduction, quantization, and dimension pruning before clustering. Results show that using 1-bit codes achieves near-optimal clustering quality (within 1% of ideal) while reducing storage requirements by 60x.
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