UNSUPERVISED LEARNING OF 3D OBJECTS CONSERVING GLOBAL TOPOLOGICAL ORDER

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.