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

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 metric-space 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

Towards Proximity Graph Auto-configuration - An Approach Based on Meta-learning

Due to the high production of complex data, the last decades have provided a huge advance in the development of similarity search methods. Recently graph-based methods have outperformed other ones in the literature of approximate similarity search. However, a graph employed on a dataset may present different behaviors depending on its parameters. Therefore, finding a suitable graph configuration is a time-consuming task, due to the necessity to build a structure for each parameterization. Our main contribution is to save time avoiding this exhaustive process. We propose in this work an intelligent approach based on meta-learning techniques to recommend a suitable graph along with its set of parameters for a given dataset. We also present and evaluate generic and tuned instantiations of the approach using Random Forests as the meta-model. The experiments reveal that our approach is able to perform high quality recommendations based on the user preferences.</p