DocumentCode :
323384
Title :
A new competitive learning algorithm for vector quantization based on the neuron winning probability
Author :
Yong, Xu ; Guangqun, Yan ; Hexin, Chen ; Yisong, Dai
Author_Institution :
Dept. of Sci. & Technol., Changchun Inst. of Posts & Telecommun., China
Volume :
1
fYear :
1997
fDate :
28-31 Oct 1997
Firstpage :
485
Abstract :
Neural network competitive learning algorithms are widely used for vector quantization. Some typical competitive learning algorithms have been specially investigated, analyzed and their performances have also been evaluated. A new competitive learning algorithm based on the neuron winning probability is presented for vector quantization. Unlike the traditional competitive learning algorithms where only one neuron will win and learn in each competition, every neuron in the proposed probability sensitive competitive learning algorithm (PSCL) will win to some extent, depending on its winning probability and adjustment of distortion distance to the input vector. The new algorithm is shown to be efficient to overcome the problem of neuron underutilization
Keywords :
neural nets; probability; signal processing; unsupervised learning; vector quantisation; competitive learning algorithm; distortion distance; input vector; neural network competitive learning algorithms; neuron underutilization; neuron winning probability; probability sensitive competitive learning algorithm; vector quantization; Algorithm design and analysis; Clustering algorithms; Distortion measurement; Lakes; Neural networks; Neurons; Performance analysis; Performance evaluation; Telecommunications; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-4253-4
Type :
conf
DOI :
10.1109/ICIPS.1997.672829
Filename :
672829
Link To Document :
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