Non-metric Similarity Search Using Genetic TriGen

Abstract

The metric space model is a popular and extensible model for indexing data for fast similarity search. However, there is often need for broader concepts of similarities (beyond the metric space model) while these cannot directly benefit from metric indexing. This paper focuses on approximate search in semi-metric spaces using a genetic variant of the TriGen algorithm. The original TriGen algorithm generates metric modifications of semi-metric distance functions, thus allowing metric indexes to index non-metric models. However, “analytic” modifications provided by TriGen are not stable in predicting the retrieval error. In our approach, the genetic variant of TriGen – the TriGenGA – uses genetically learned semi-metric modifiers (piecewise linear functions) that lead to better estimates of the retrieval error. Additionally, the TriGenGA modifiers result in better overall performance than original TriGen modifiers.