Dimensionality-Reduction Using Connectionist Networks,
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1987
- Accession Number
- ADA183632
Entities
People
- Eric Saund
Organizations
- Massachusetts Institute of Technology