  This paper defines a new learning architecture, Layered Self-Organizing Maps
(LSOMs), that uses the SOM and supervised-SOM learning algorithms. The
architecture is validated with the MNIST database of hand-written digit images.
LSOMs are similar to convolutional neural nets (covnets) in the way they sample
data, but different in the way they represent features and learn. LSOMs analyze
(or generate) image patches with maps of exemplars determined by the SOM
learning algorithm rather than feature maps from filter-banks learned via
backprop.
  LSOMs provide an alternative to features derived from covnets. Multi-layer
LSOMs are trained bottom-up, without the use of backprop and therefore may be
of interest as a model of the visual cortex. The results show organization at
multiple levels. The algorithm appears to be resource efficient in learning,
classifying and generating images. Although LSOMs can be used for
classification, their validation accuracy for these exploratory runs was well
below the state of the art. The goal of this article is to define the
architecture and display the structures resulting from its application to the
MNIST images.
