Efficient Approximate Vector Search in High-Dimensional Spaces

PhD Qualifying Examination


Title: "Efficient Approximate Vector Search in High-Dimensional Spaces"

by

Mr. Xi ZHAO


Abstract:

Vector Search in high-dimensional spaces, that aims to find the similar item to 
a given query vector, is a classic but important problem in the database. In 
this survey, we investigate two kinds of widely used vector search-- 
approximate nearest neighbor search and maximum inner product search, and 
provide a comprehensive review of significant research findings from the past. 
Then, we delve into notable works on vector search, analyzing their respective 
strengths and limitations. Furthermore, the article presents an outline of 
potential future research directions in vector search.


Date:                   Monday, 3 June 2024

Time:                   2:00pm - 4:00pm

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:      Prof. Xiaofang Zhou (Supervisor)
                        Prof. Raymond Wong (Chairperson)
                        Prof. Ke Yi
                        Prof. Bolong Zheng (HUST)