Title :
Generalization of topology preserving maps: a graph approach
Author_Institution :
Dept. of Photogrammetry & Geoinformatics, Budapest Univ. of Technol. & Econ., Hungary
Abstract :
The work presents a novel algorithm, which is based on the self-organizing map (SOM) method. The combination of an undirected acyclic graph with the Kohonen learning rule results the efficient self-organizing neuron graph (SONG) algorithm. It has two modi: one is based on the adjacency information of the neuron graph, the other integrates an all-pair shortest path function, which permanently updates a generalized distance matrix. The newly developed SONG techniques were involved in pattern recognition tasks, where they proved their efficiency and flexibility.
Keywords :
graph theory; matrix algebra; pattern recognition; self-organising feature maps; Kohonen learning rule; distance matrix; neuron graph; pattern recognition; self-organizing map method; self-organizing neuron graph algorithm; topology preserving maps; undirected acyclic graph; Artificial neural networks; Books; Lattices; Mathematical model; Mathematics; Network topology; Neurons; Organizing; Paper technology; Pattern recognition;
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Print_ISBN :
0-7803-8359-1
DOI :
10.1109/IJCNN.2004.1380025