@Article{rojer1989:multiple-map,
author = {Rojer, Alan S. and Schwartz, Eric L.},
title = {A multiple-map model for pattern classification},
journal = {Neural Computation},
year = 1989,
volume = 1,
number = 1,
pages = {104--115},
month = {Spring},
datestr = 198905,
INSPEC = 3683362,
abstract = {A characteristic feature of the vertebrate sensory
cortex (and midbrain) is the existence of multiple
two-dimensional map representations. The authors
have constructed a multiple-map classifier, which
permits abstraction of the computational properties
of a multiple-map architecture. They identify three
problems which characterize a multiple-map
classifier: classification in two dimensions,
mapping from high dimensions to two dimensions, and
combination of multiple maps. They demonstrate
component solutions to each of the problems, using
Parzen-window density estimation in two dimensions,
a generalized Fisher discriminant function for
dimensionality reduction, and split/merge methods to
construct a 'tree of maps' for the multiple-map
representation. The combination of components is
modular and each component could be improved or
replaced without affecting the other components. The
classifier training procedure requires time which is
linear in the number of training examples;
classification time is independent of the number of
training examples and requires constant
space. Performance of this classifier on Fisher's
(1936) iris data, Gaussian clusters on a
five-dimensional simplex, and digitized speech data
is comparable to competing algorithms, such as
nearest-neighbor, back-propagation and Gaussian
classifiers. This work provides an example of the
computational utility of multiple-map representation
for classification. It is one step towards the goal
of understanding why brain areas such as the visual
cortex utilize multiple map-like representations of
the world.},
keywords = {brain models; classification; pattern recognition;
vision}
}