Vector Dimensionality Reduction

Techniques for reducing embedding dimensions while preserving semantic information, including PCA, random projection, and learned compression methods like Matryoshka embeddings. Dimensionality reduction enables faster search, lower storage costs, and efficient deployment at scale.