Approximate Nearest Neighbors Search Library
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An Approximate Nearest Neighbors Search Library is a search library that facilitates approximate nearest neighbor search.
- Context:
- It can efficiently search for points in space that are close to a given query point, typically in a high-dimensional space.
- It can employ various algorithms and data structures, such as Static Trees, Random Projection Trees, and Search Algorithms, to quickly approximate the nearest neighbors.
- It can handle very large datasets that do not fit into memory, often by mapping data to disk.
- It can support a variety of distance metrics, including Euclidean distance, Manhattan distance, and cosine similarity.
- It is particularly valuable in scenarios where the balance between accuracy and speed is crucial, such as in real-time applications.
- It can (often) decouple the index creation from the lookup process, allowing for the distribution and sharing of indexes as static files across processes.
- Examples of such libraries include Annoy (Approximate Nearest Neighbors Oh Yeah) Library, FAISS, and HNSW.
- ...
- Example(s):
- Annoy (Approximate Nearest Neighbors Oh Yeah) Library: Supports various distance metrics and can work with data that doesn't fit into memory.
- FAISS (Facebook AI Similarity Search): Optimized for efficiency on GPU.
- HNSW (Hierarchical Navigable Small World): Known for its balance between search efficiency and accuracy.
- ...
- Counter-Example(s):
- Exact nearest neighbor search libraries, such as KD-tree based libraries, which do not approximate but seek to find the exact nearest neighbors.
- Algorithms used for different purposes, such as Dynamic Time Warping (DTW) for time-series similarity, not suitable for high-dimensional space nearest neighbor searches.
- See: Nearest Neighbor Search, high-dimensional space, Big Data Analytics, Feature Hashing, Indexing Algorithm, Query Optimization, Vector Space Model.