Random indexing explained with high probability

Abstract

Random indexing (RI) is an incremental method for constructing a vector space model (VSM) with a reduced dimensionality. Previously, the method has been justified using the mathematical framework of Kanerva’s sparse distributed memory. This justification, although intuitively plausible, fails to provide the information that is required to set the parameters of the method. In order to suggest criteria for the method’s parameters, the RI method is revisited and described using the principles of linear algebra and sparse random projections in Euclidean spaces. These simple mathematics are then employed to suggest criteria for setting the method’s parameters and to explain their influence on the estimated distances in the RI-constructed VSMs. The empirical results observed in an evaluation are reported to support the suggested guidelines in the paper.