Associative vector storage system supporting fast similarity search based on self-similarity feature extractions across multiple transformed domains

Abstract

An associative vector storage system has an encoding engine that takes input vectors, and generates transformed coefficients for a tunable number of iterations. Each iteration performs a complete transformation to obtain coefficients, thus performing a process of iterative transformations. The encoding engine selects a subset of coefficients from the coefficients generated by the process of iterative transformations to form an approximation vector with reduced dimension. A data store stores the approximation vectors with a corresponding set of meta data containing information about how the approximation vectors are generated. The meta data includes one or more of the number of iterations, a projection map, quantization, and statistical information associated with each approximation vector. A search engine uses a comparator module to perform similarity search between the approximation vectors and a query vector in a transformed domain. The search engine uses the meta data in a distance calculation of the similarity search.