Accession Number : ADA183632

Title :   Dimensionality-Reduction Using Connectionist Networks,

Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB

Personal Author(s) : Saund,Eric

PDF Url : ADA183632

Report Date : Jan 1987

Pagination or Media Count : 28

Abstract : A method is presented for using connectionist networks of simple computing elements to discover a particular type of constraint in multidimensional data. Suppose that some data source provides samples consisting of n dimensional feature vectors, but that this data all happens to lie on an m dimensional surface embedded in the n dimensional feature space. Then occurrences of data can be more concisely described by specifying an m dimensional location on the embedded surface than by reciting all n components of the feature vector. The recording of data in such a way is known as dimensional reduction. This paper describes a method for performing dimensionality reduction in a wide class of situations for which an assumption of linearity need not be made about the underlying constraint surface. The method takes advantage of self organizing properties of connectionist networks of simple computing elements. We present a scheme for representing the values of continuous (scalar) variables in subsets of units. The back propagation 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.

Descriptors :   *DATA REDUCTION, PATTERN RECOGNITION, SCALAR FUNCTIONS, VARIABLES, AUXILIARY, DATA PROCESSING, EMBEDDING, PRESSURE, RECORDING SYSTEMS, SELF ORGANIZING SYSTEMS, SIZES(DIMENSIONS), SURFACES

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE