LiteHST: A Tree Embedding based Method for Similarity Search
Summary: LiteHST: tree-embedding index for kNN under arbitrary metrics. Directly uses the HST tree for search (no embedding index) with a faster construction, lower time, and optimal distance bounds, plus reductions in distance computations; outperforms SOTA. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yuxiang Zeng
- 2. Yongxin Tong
- 3. Lei Chen
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,837 | GTI: Graph-based Tree Index with Logarithm Updates for Nearest Neighbor Search in High-Dimensional Spaces | 2025 | VLDB | 4.6379694e-05 |
| 8,171 | GTS: GPU-based Tree Index for Fast Similarity Search | 2024 | SIGMOD | 4.5688498e-05 |
| 10,111 | Scalable Graph Indexing using GPUs for Approximate Nearest Neighbor Search | 2026 | SIGMOD | 4.1945683e-05 |
| 10,224 | SVFusion: A CPU-GPU Co-Processing Architecture for Large-Scale Real-Time Vector Search | 2026 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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