To compress vectors without losing accuracy, focus on techniques that reduce storage or computation needs while preserving the vector’s essential information. Effective methods include dimensionality reduction, quantization, and pruning. These approaches maintain the vector’s utility in tasks like similarity search or machine learning by strategically removing redundant or less impactful data. The key is to balance compression with minimal impact on performance metrics relevant to your use case.
One practical approach is using dimensionality reduction techniques like PCA (Principal Component Analysis) or autoencoders. PCA identifies axes of maximum variance in your data and projects vectors onto a lower-dimensional space. For example, compressing a 300-dimensional word embedding to 100 dimensions while retaining 95% of variance. Autoencoders, neural networks trained to reconstruct inputs through a bottleneck layer, can capture non-linear patterns. If trained properly on representative data, they maintain relationships between vectors. Another method is product quantization, which divides a vector into subvectors, clusters each subset, and replaces original values with cluster indices. This is widely used in search systems—compressing 128D image embeddings to 8 bytes with <2% recall drop on benchmark datasets.
Quantization and pruning offer additional gains. Scalar quantization converts 32-bit floating-point values to 8-bit integers by mapping value ranges—useful when precision beyond 0.1% isn’t needed. For instance, reducing a vector’s memory footprint by 75% with minimal effect on cosine similarity calculations. Pruning removes near-zero elements in sparse vectors, like zeroing out 90% of transformer model embeddings with <1% accuracy loss in NLP tasks. Combining techniques often works best: first reduce dimensions via PCA, then apply product quantization. Always validate using your actual workload—test compressed vectors on retrieval accuracy, classification performance, or other domain-specific metrics before finalizing the approach. Compression parameters (like PCA dimensions or quantization bits) should be tuned based on these empirical results.