Loading [a11y]/accessibility-menu.js
Best basis selection using sparsity driven multi-family wavelet transform | IEEE Conference Publication | IEEE Xplore

Best basis selection using sparsity driven multi-family wavelet transform


Abstract:

A common tool for time-frequency analysis is based on wavelets. However, these representations are computed with only one wavelet family and thus might not be able to mat...Show More

Abstract:

A common tool for time-frequency analysis is based on wavelets. However, these representations are computed with only one wavelet family and thus might not be able to match exactly with different features of various frequency bands. We thus introduce the Multi-family Discrete Wavelet Transform, a computationally tractable adaptive wavelet transform leading to sparse optimized representations which will select the optimal wavelet family at each frequency band. The approach is an optimized Discrete Wavelet Transform where at each level of the decomposition the best wavelet basis is selected as well as the optimum depth by means of information theory tools. This development is motivated by the need to have an unsupervised sparse representation of a priori unknown signals. Our representation is finally applied on intracranial EEG (iEEG) data in order to show its efficacy.
Date of Conference: 07-09 December 2016
Date Added to IEEE Xplore: 24 April 2017
ISBN Information:
Conference Location: Washington, DC, USA

Contact IEEE to Subscribe

References

References is not available for this document.