Product Quantization: Compressing high-dimensional vectors by 97%
a year ago
- #data-compression
- #machine-learning
- #vector-search
- Product Quantization (PQ) is a method for compressing high-dimensional vectors, reducing memory usage by up to 97%.
- PQ splits vectors into subvectors, assigns each to the nearest centroid, and replaces them with IDs, significantly cutting memory footprint.
- Combining PQ with Inverted File (IVF) indexing (IVFPQ) speeds up searches by 92x compared to non-quantized indexes without losing accuracy.
- PQ is implemented in libraries like Faiss and services like Pinecone, offering efficient vector search capabilities.
- Quantization differs from dimensionality reduction by focusing on reducing the scope of possible values rather than the number of dimensions.
- PQ's memory efficiency and speed come at the cost of recall accuracy, which can be mitigated by adjusting parameters like nbits and nprobe.
- The Sift1M dataset example demonstrates PQ's practical application, showing significant improvements in search speed and memory usage.
- IVFPQ indexes further optimize search by restricting the search scope to the nearest Voronoi cells, balancing speed and recall.