High-dimensional similarity retrieval using dimensional choice

There are several pieces of information that can be utilized in order to improve the efficiency of similarity searches on high-dimensional data. The most commonly used information is the distribution of the data itself, but the use of dimensional choice based on the information in the query as well as the parameters of the distribution can provide an effective improvement in the query processing speed and storage. The use of this method can produce dimension reduction by as much as a factor of n, the number of data points in the database, over sequential search. We demonstrate that the curse of dimensionality is not based on the dimension of the data itself, but primarily upon the effective dimension of the distance fiinction. We also introduce a new distance function that utilizes fewer dimensions of the higher dimensional space to produce a maximal lower bound distance in order to approximate the fidl distance function. This work has demonstrated significant dimension reduction, up to 70% reduction with an improvement in accuracy or over 99% with only a 6% loss in accuracy on a prostate cancer data set.