Machine Learning and High Dimensional Vector Search

This technical report discusses the relationship between machine learning (ML) and high-dimensional vector search (VS), exploring the limited impact of ML on VS despite their parallel development in research. The document outlines various methods for high-dimensional vector search, including statistical tools, signal processing approaches, and graph traversal algorithms. It examines the use of ML in VS applications, such as image retrieval, and presents different ML-based approaches for optimizing vector search processes. The report also highlights the challenges faced when integrating ML with VS, particularly regarding computational efficiency and accuracy. It details how vector search can be viewed as a linear classification problem and discusses the potential for ML to improve vector distribution modeling. The report concludes with a review of methods that incorporate VS within deep learning models, illustrating the complexities and limits of applying ML techniques in the context of vector search.