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

An associative vector storage system, comprising:

• an encoding engine that takes input vectors, and generates transformed coefficients for a tunable number of iterations, wherein each iteration performs a complete transformation to obtain coefficients, thereby performing a process of iterative transformations, wherein said 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 that stores the approximation vectors with a corresponding set of meta data containing information about how the approximation vectors are generated, wherein the meta data includes at least one of the number of iterations, a projection map, quantization, or statistical information associated with each approximation vector;
• and
• a search engine that uses a comparator module to perform similarity search between the approximation vectors and a query vector in a transformed domain, wherein said search engine uses the meta data in a distance calculation of the similarity search.