Jh. Chao et al., UNSUPERVISED LEARNING OF 3D OBJECTS CONSERVING GLOBAL TOPOLOGICAL ORDER, IEICE transactions on fundamentals of electronics, communications and computer science, E76A(5), 1993, pp. 749-753
The self-organization rule of planar neural networks has been proposed
for learning of 2D distributions. However, it cannot be applied to 3D
objects. In this paper, we propose a new model for global representat
ion of the 3D objects. Based on this model, global topology reserving
self-organization is achieved using parallel local competitive learnin
g rule such as Kohonen's maps. The proposed model is able to represent
the objects distributively and easily accommodate local features.