Overview of Nearest Neighbor Search Algorithms
The article reviews how high‑dimensional vector representations in deep‑learning applications require efficient approximate nearest‑neighbor search, comparing K‑d trees, hierarchical k‑means trees, locality‑sensitive hashing, product quantization, and HNSW graphs, and discusses practical FAISS implementations and how algorithm choice depends on data size, recall, latency, and resources.