The generalized dimensionality reduction problem

Abstract

The dimensionality reduction problem has been widely studied in the database literature because of its application for concise data representation in a variety of database applications. The main focus in dimensionality reduction is to represent the data in a smaller number of dimensions that the least amount of information is lost. In this paper, we study the dimensionality reduction problem from an entirely different perspective. We discuss methods to find a representation of the data so that a user-defined objective function is optimized. For example, we may desire to find a reduction of the data in which a particular kind of classifier works effectively. Another example (relevant to the similarity search domain) would be a reduction in which the cluster of k closest points provides the best distance based separation from the remaining data set. We discuss a general abstraction for the problem and provide the broad framework of an evolutionary algorithm which solves this abstraction. We test our framework on two separate instantiations of this framework and provide results illustrating the effectiveness and efficiency of our method. Copyright © by SIAM.