DocumentCode :
2695609
Title :
Self-organizing hierarchical feature maps
Author :
Koikkalainen, Pasi ; Oja, Erkki
fYear :
1990
fDate :
17-21 June 1990
Firstpage :
279
Abstract :
The topological feature map (TFM) algorithm introduced by T. Kohenen (1982) implements two important properties: a vector quantization (VQ) and a topology-preserving mapping. A tree-structured TFM (TSTFM) is presented as a computationally inexpensive alternative to the TFM algorithm. The computational complexity of the TSTFM is O (log N) rather than O(N) for the TFM. In addition, the TSTFM has some new properties that prove to be useful for VQ and in the context of visual perception: increased performance in VQ compared to the tree-structured VQ of A. Buzo et al. (1980) and hierarchical mapping of code vectors
Keywords :
computational complexity; neural nets; self-adjusting systems; TFM algorithm; computational complexity; neural nets; self-organising hierarchical feature maps; topological feature map; topology-preserving mapping; tree-structured TFM; unsupervised learning; vector quantization; visual perception;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1990., 1990 IJCNN International Joint Conference on
Conference_Location :
San Diego, CA, USA
Type :
conf
DOI :
10.1109/IJCNN.1990.137727
Filename :
5726686
Link To Document :
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