Lossy reduction for very high dimensional data

Abstract

We consider the use of data reduction techniques for the problem of approximate query answering. We focus on applications for which accurate answers to selective queries are required, and for which the data are very high dimensional (having hundreds of attributes). We present a new data reduction method for this type of application, called the RS kernel. We demonstrate the effectiveness of this method for answering difficult, highly selective queries over high dimensional data using several real datasets.