Accurate and Fast Retrieval for Complex Non-metric Data via Neighborhood Graphs

Abstract

We demonstrate that a graph-based search algorithm-relying on the construction of an approximate neighborhood graph-can directly work with challenging non-metric and/or non-symmetric distances without resorting to metricspace mapping and/or distance symmetrization, which, in turn, lead to substantial performance degradation. Although the straightforward metrization and symmetrization is usually ineffective, we find that constructing an index using a modified, e.g., symmetrized, distance can improve performance. This observation paves a way to a new line of research of designing index-specific graph-construction distance functions. This is an archival version, the publisher's version is available at Springer.com.

Flexible retrieval with NMSLIB and FlexNeuART

“…Graph-based retrieval algorithms have been shown to work efficiently for a variety of non-metric and non-symmetric distances (Boytsov and Nyberg, 2019a;Boytsov, 2018;Naidan et al, 2015b). This flexibility permits adding new distances/similarities with little effort (as we do not have to change the retrieval algorithms).…”