# binary

> Browse 4 items tagged with binary.

## Items

- [Binary Quantization for Vector Search](https://vectordb.ever.works/items/binary-quantization-for-vector-search) — Compression technique that converts full-precision vectors to binary representations, achieving 32x storage reduction while maintaining 90-95% recall for efficient large-scale vector search.
- [Hamming Distance for Binary Vector Search](https://vectordb.ever.works/items/hamming-distance-for-binary-vector-search) — Distance metric for comparing binary vectors using XOR operations, enabling efficient similarity search with dramatically reduced storage requirements compared to full-precision vectors.
- [LLMs Meet Isolation Kernel](https://vectordb.ever.works/items/llm-isolation-kernel-binary-embeddings) — A research paper introducing lightweight, learning-free binary embeddings for fast retrieval. The approach uses isolation kernels to generate binary embeddings that dramatically reduce storage requirements (32× compression) while maintaining retrieval quality.
- [Hamming Distance](https://vectordb.ever.works/items/hamming-distance) — A distance metric that measures the number of positions at which corresponding elements in two vectors differ. Particularly useful for binary vectors and categorical data, commonly used with binary quantization in vector search.

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