Dimensionality-Reduction Using Connectionist Networks

Abstract

This paper presents a method for using the self-organizing properties of connectionist networks of simple computing elements to discover a particular type of constraint in multidimensional data. The method performs dimensionality-reduction in a wide class of situations for which an assumption of linearity need not be made about the underlying constraint surface. We present a scheme for representing the values of continuous (scalar) variables in subsets of units. The backpropagation weight updating method for training connectionist networks is extended by the use of auxiliary pressure in order to coax hidden units into the prescribed representation for scalar-valued variables.