Dimensionality Reduction in Sentence Transformer Vector Databases with Fast Fourier Transform

arXiv:2404.06278v1 Announce Type: cross
Abstract: Dimensionality reduction in vector databases is pivotal for streamlining AI data management, enabling efficient storage, faster computation, and improved model performance. This paper explores the benefits of reducing vector database dimensions, with a focus on computational efficiency and overcoming the curse of dimensionality. We introduce a novel application of Fast Fourier Transform (FFT) to dimensionality reduction, a method previously underexploited in this context. By demonstrating its utility across various AI domains, including Retrieval-Augmented Generation (RAG) …

abstract ai data arxiv benefits computation computational cs.ai cs.cl cs.db cs.lg data database databases data management dimensionality dimensions efficiency enabling faster focus fourier management paper performance pivotal storage the curse of dimensionality transformer type vector vector database vector databases