keyword

vector compression

Vector compression is the process of reducing the storage size and computational complexity of high-dimensional numerical vectors while preserving the geometric and similarity relationships between them. In domains such as machine learning, computer vision, and information retrieval, complex data objects like images and text are commonly transformed into dense, high-dimensional vector embeddings. Vector compression techniques, such as dimensionality reduction, scalar quantization, product quantization, and hashing, convert these continuous floating-point vectors into compact codes or lower-dimensional representations. By substantially lowering memory requirements and accelerating distance calculations, vector compression enables scalable storage, efficient indexing, and rapid nearest-neighbor search across massive datasets with minimal loss in retrieval accuracy.

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