A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Functional classification in Hilbert spaces
IEEE Transactions on Information Theory
Signal Subspace Separation Based on the Divergence Measure of a Set of Wavelets Coefficients
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Feature selection for classification of oscillating time series
Expert Systems: The Journal of Knowledge Engineering
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A wavelet-based procedure for clustering signals is proposed. It combines an individual signal preprocessing by wavelet denoising, a dimensionality reduction step by wavelet compression and a classical clustering strategy applied to a suitably chosen set of wavelet coefficients. The ability of wavelets to cope with signals of arbitrary or time-dependent regularity as well as to concentrate signal energy in few large coefficients, offers a useful tool to carry out both significant noise reduction and efficient compression. A simulated example and an electrical dataset are considered to illustrate the value of introducing wavelets for clustering such complex data.