  In this short paper, a neural network that is able to form a low dimensional
topological hidden representation is explained. The neural network can be
trained as an autoencoder, a classifier or mix of both, and produces different
low dimensional topological map for each of them. When it is trained as an
autoencoder, the inherent topological structure of the data can be visualized,
while when it is trained as a classifier, the topological structure is further
constrained by the concept, for example the labels the data, hence the
visualization is not only structural but also conceptual. The proposed neural
network significantly differ from many dimensional reduction models, primarily
in its ability to execute both supervised and unsupervised dimensional
reduction. The neural network allows multi perspective visualization of the
data, and thus giving more flexibility in data analysis. This paper is
supported by preliminary but intuitive visualization experiments.
