Research map / Learning theory and algorithms / Combinatorial and search optimization
Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace space partitioning (LSH-style hashing) with dynamic continuous indexing based on random 1D projections | k-nearest neighbour retrieval · CIFAR-100 and MNIST, 25-NN | candidate points needed for given approximation ratio: 61.3% - 78.7% fewer candidate points than LSH | Helped |
| use DCI instead of LSH for memory footprint | k-nearest neighbour retrieval · CIFAR-100 and MNIST | memory usage: less than 1/20 of LSH memory | Helped |
| LSH degrades on dense-cluster datasets with varying density | LSH baseline · MNIST vs CIFAR-100 | candidate points / approximation quality | Hurt |
Closest papers by meaning
- Fast k-Nearest Neighbour Search via Prioritized DCI (ICML 2017 · similarity 0.94)
- FANNG: Fast Approximate Nearest Neighbour Graphs (CVPR 2016 · similarity 0.88)
- Fast Diverse Nearest Neighbor Search (NeurIPS 2026 · similarity 0.88)
- Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search (NeurIPS 2022 · similarity 0.88)
- Graph-Based Algorithms for Diverse Similarity Search (ICML 2025 · similarity 0.87)
- MCGI: Manifold-Consistent Graph Indexing for Billion-Scale Disk-Resident Vector Search (NeurIPS 2026 · similarity 0.86)
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