mlsanityData to 7 Oct 2026

Dimensionality Reduction in Sentence Transformer Vector Databases with Fast Fourier Transform

arXiv 2404.06278 ↗First posted 9 Apr 2024cs.DB cs.AI cs.CLcited by 1

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Abstract (arXiv)

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) models and image processing, this FFT-based approach promises to improve data retrieval processes and enhance the efficiency and scalability of AI solutions. The incorporation of FFT may not only optimize operations in real-time processing and recommendation systems but also extend to advanced image processing techniques, where dimensionality reduction can significantly improve performance and analysis efficiency. This paper advocates for the broader adoption of FFT in vector database management, marking a significant stride towards addressing the challenges of data volume and complexity in AI research and applications. Unlike many existing approaches, we directly handle the embedding vectors produced by the model after processing a test input.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 9 Apr 2024.

Builds on

6 of 16 references.

References · 6

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  4. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  5. 2017Simple and Effective Dimensionality Reduction for Word Embeddingscited by 38
  6. 2024Evaluating Unsupervised Dimensionality Reduction Methods for Pretrained Sentence Embeddingscited by 6

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Optimization of Retrieval-Augmented Generation Context with Outlier Detectioncited by 1

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  2. 2025PCA-RAG: Principal Component Analysis for Efficient Retrieval-Augmented Generation
  3. 2025Exploring Distributed Vector Databases Performance on HPC Platforms: A Study with Qdrant
  4. 2026Filtered Approximate Nearest Neighbor Search in Vector Databases: System Design and Performance Analysis
  5. 20254bit-Quantization in Vector-Embedding for RAG
  6. 2025Optimization of embeddings storage for RAG systems using quantization and dimensionality reduction techniques
  7. 2025Breaking the Curse of Dimensionality: On the Stability of Modern Vector Retrieval
  8. 2026FaTRQ: Tiered Residual Quantization for LLM Vector Search in Far-Memory-Aware ANNS Systems