Most research into similarity search in metric spaces relies upon the triangle inequality property. This property allows the space to be arranged according to relative distances to avoid searching some subspaces. We show that many common metric spaces, notably including those using Euclidean and Jensen-Shannon distances, also have a stronger property, sometimes called the four-point property: in essence, these spaces nallow an isometric embedding of any four points in three-dimensional Euclidean space, as well as any three points in two-dimensional Euclidean space. In fact, we show that any space which is isometrically embeddable in Hilbert space has the stronger property. This property gives stronger geometric guarantees, and one in particular, which we name the Hilbert Exclusion property, allows any indexing mechanism which uses hyperplane partitioning to perform better. One outcome of this observation is that a number of state-of-the-art
indexing mechanisms over high dimensional spaces can be easily extended to give a significant increase in performance; furthermore, the improvement given is greater in higher dimensions. This therefore leads to a significant improvement in the cost of metric search in these spaces.

Keywords: {Multidimensional range search; Proximity search; Database query processing; Information retrieval query processing; Retrieval models and ranking; Retrieval efficiency; Multimedia information systems; Random projections and metric embeddings; Similarity search; Metric Space; Metric Indexing; Four-point property;Hilbert Embedding}

File: https://dl.acm.org/citation.cfm?id=3001583