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Author(s): Campos, M.M. | Carpenter, G.A. |
Year: 1998
Citation: Proceedings of the International Joint Conference on Neural Networks (IJCNN 98), Piscataway, NJ: IEEE Press, 763-767.
Abstract: The WSOM (wavelet self-organizing map) model, a neural network for the creation of wavelet bases adapted to the distribution of input data, is introduced. The model provides an efficient online method to construct high-dimensional wavelet bases. Simulations of a 1D function approximation problem illustrate how WSOM adapts to non-uniformly distributed input data, outperforming the discrete wavelet transform. A speaker-independent vowel recognition benchmark task demonstrates how the model constructs high-dimensional bases using low-dimensional wavelets.
Topics:
Other,
Applications:
Other,
Models:
Self Organizing Maps,
