Unverified50% confidenceFactExact time
局部敏感哈希（LSH）通过设计哈希函数使相似向量高概率映射到同一哈希桶，将最近邻搜索的时间复杂度从线性降至近似常数级
1
Sources
50%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
Related Claims
Unverified局部敏感哈希LSH由Indyk和Motwani于1998年提出，可将billion级向量的最近邻搜索时间复杂度从O(n)降至近似O(1)65% similarVerifiedHNSW reduces search complexity to approximately O(log n) by constructing a multi-layer graph index, while IVF partitions the vector space via K-means clustering to achieve orders-of-magnitude speed improvements at the cost of approximately 5–10% recall.62% similarUnverifiedApproximate nearest-neighbor algorithms such as HNSW (Hierarchical Navigable Small World graphs) can achieve millisecond-level response times at vector scales of tens of millions.59% similarUnverified代数哈希的响应时间在统计上高度稳定，其P99尾延迟可被严格数学界定，而神经网络推理的延迟随输入复杂度、KV缓存状态和批处理大小动态波动58% similarUnverified对MoE模型的量化需要格外谨慎，因稀疏激活使专家参数数值分布集中，低比特量化易造成路由偏差导致能力退化57% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/517747API
curl https://kongchang.com/api/v1/knowledge/claims/517747MCP
get_claim(id=517747)