Issue No. 03 - March (1989 vol. 11)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.21799
<p>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 of the embedded surface than by reciting all n components of the feature vector. The recording of data in such a way is known as dimensionality-reduction. A method is presented 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. The authors present a scheme for representing the values of continuous (scalar) variables in subsets of units.</p>
pattern recognition; data abstraction; backpropagation; connectionist networks; multidimensional data; feature-vectors; feature space; dimensionality-reduction; artificial intelligence; computerised pattern recognition
E. Saund, "Dimensionality-Reduction Using Connectionist Networks," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 11, no. , pp. 304-314, 1989.