A competitive wavelet network for signal clustering

This correspondence proposes a novel signal clustering method based on the unsupervised training of a wavelet network. The synaptic weights are parameterized by wavelet basis functions, which are adjusted by a competitive algorithm that makes use of the neighborhood concept proposed by Kohonen. The...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2004-04, Vol.34 (2), p.1282-1288
Main Authors: Galvao, R.K.H., Yoneyama, T.
Format: Article
Language:English
Subjects:
Clustering
Clustering algorithms
Clustering methods
Computer networks
Cybernetics
Data preprocessing
Dynamical systems
Dynamics
Networks
Neural networks
Neurons
Noise robustness
Signal resolution
Training
Vectors
Wavelet
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Summary:This correspondence proposes a novel signal clustering method based on the unsupervised training of a wavelet network. The synaptic weights are parameterized by wavelet basis functions, which are adjusted by a competitive algorithm that makes use of the neighborhood concept proposed by Kohonen. The robustness of the wavelet network with respect to noise is illustrated in a simulated problem, in which dynamic systems are grouped on the basis of their step responses. An example involving clustering of electrocardiographic signals taken from the MIT-BIH database is also presented. In this case, the ability of the proposed network to perform clustering at successive resolution levels is illustrated. The possibility of interpreting the information encoded in the network at the end of training is also discussed.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2003.817104