SplitNet: learning of tree structured Kohonen chains

This work introduces a tree structured neural network model for topology preserving vector quantization with one-dimensional Kohonen chains. The leaves of the tree are the chains, each of which quantizes a subspace of the input space. Topological defects can effectively be detected and splitting of the chain at that location results in a growing of the tree structure and increase of topology preservation. Additionally, the chains are able to grow and shrink in order to approximate user defined criteria. Advantages over existing dynamic network models are the flexible tree structure, the total lack of global parameters or calculations as well as the simulation and retrieval speed due to the network structure. Different levels of generalization and prototypicality are naturally observed.

[1]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[2]  R. Der,et al.  A new quantitative measure of topology preservation in Kohonen's feature maps , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[3]  Wei-Chung Lin,et al.  Dynamic hierarchical self-organizing neural networks , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[4]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[5]  Pasi Koikkalainen,et al.  Self-organizing hierarchical feature maps , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[6]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[7]  J. Lampinen,et al.  Fast self-organization by the probing algorithm , 1989, International 1989 Joint Conference on Neural Networks.

[8]  Pasi Koikkalainen,et al.  Progress with the Tree-Structured Self-Organizing Map , 1994, ECAI.

[9]  A. Jennings,et al.  Structurally adaptive self-organizing neural trees , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[10]  Michael Kelly.  SELF-ORGANIZING MAP TRAINING USING DYNAMIC K-D TREES , 1991 .