Searching for high-dimensional vector data with high accuracy is an
inevitable search technology for various types of data. Graph-based indexes are
known to reduce the query time for high-dimensional data. To further improve
the query time by using graphs, we focused on the indegrees and outdegrees of
graphs. While a sufficient number of incoming edges (indegrees) are
indispensable for increasing search accuracy, an excessive number of outgoing
edges (outdegrees) should be suppressed so as to not increase the query time.
Therefore, we propose three degree-adjustment methods: static degree adjustment
of not only outdegrees but also indegrees, dynamic degree adjustment with which
outdegrees are determined by the search accuracy users require, and path
adjustment to remove edges that have alternative search paths to reduce
outdegrees. We also show how to obtain optimal degree-adjustment parameters and
that our methods outperformed previous methods for image and textual data.

研究高维向量数据的查询技术，提出了三种度调整方法并给出了优化参数，经验证，在高维图像和文本数据的检索准确性方面表现优于以前的方法。