DocumentCode :
3379996
Title :
Self-organizing topological tree
Author :
Xu, Pengfei ; Chang, Chip-Hong
Author_Institution :
Center for High Performance Embedded Syst., Nanyang Technol. Univ., Singapore, Singapore
Volume :
5
fYear :
2004
fDate :
23-26 May 2004
Abstract :
The self-organizing maps (SOM) introduced by Kohonen implemented two important operations: vector quantization (VQ) and a topology-preserving mapping. In this paper, a tree structured SOM algorithm named self-organizing topological tree (SOTT) is proposed. Unlike the conventional tree structured SOMs as stated in T. Kohonen (2001), P. Koikkalainnen and E. Oja (1990) and P. Koikkalainnen (1994), every layer of the proposed SOTT is organized simultaneously instead of layer by layer. A reduction of computational complexity from O(N) to O(logN) is achieved by the tree structured search in both the training phase and the test phase. Furthermore, such topological tree that maintains both intra-layer and inter-layer topologies during and after learning can be seen as endowing the SOM topological map with the progressive decoding and multi-resolution capabilities.
Keywords :
computational complexity; learning (artificial intelligence); self-organising feature maps; tree searching; SOM topological map; computational complexity reduction; interlayer topologies; intralayer topologies; multiresolution capabilities; progressive decoding; self-organizing maps; self-organizing topological tree; test phase; topology-preserving mapping; training phase; tree structured SOM; tree structured search; vector quantization; Artificial neural networks; Clustering algorithms; Computational complexity; Decoding; Embedded system; Network topology; Neurons; Self organizing feature maps; Testing; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 2004. ISCAS '04. Proceedings of the 2004 International Symposium on
Print_ISBN :
0-7803-8251-X
Type :
conf
DOI :
10.1109/ISCAS.2004.1329912
Filename :
1329912
Link To Document :
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