3 papers
cs.IR2025
QPAD: Quantile-Preserving Approximate Dimension Reduction for Nearest Neighbors Preservation in High-Dimensional Vector Search
Jiuzhou Fu, Dongfang Zhao
High-dimensional vector embeddings are widely used in retrieval systems, but they often suffer from noise, the curse of dimensionality, and slow runtime. However, dimensionality re…
cs.IR2025
RAE: A Neural Network Dimensionality Reduction Method for Nearest Neighbors Preservation in Vector Search
Han Zhang, Dongfang Zhao
While high-dimensional embedding vectors are being increasingly employed in various tasks like Retrieval-Augmented Generation and Recommendation Systems, popular dimensionality red…
cs.LG2025
Order-Preserving Dimension Reduction for Multimodal Semantic Embedding
Chengyu Gong, Gefei Shen, Luanzheng Guo +2
Searching for the -nearest neighbors (KNN) in multimodal data retrieval is computationally expensive, particularly due to the inherent difficulty in comparing similarity measure…