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Universal Frame Thresholding
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OA
AI
DOI：10.1109/LSP.2020.3001457.png)
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Abstract
En 中文
We provide the first frame agnostic thresholding scheme based on risk minimization, which can be applied to arbitrary frames and provide its theoretical guarantees. We investigate the proposed scheme, study its empirical risk, and demonstrates how it falls back to the standard Donoho thresholding scheme for orthogonal basis. We then validate our technique and apply it to the overcomplete wavelet transforms of the Deep Scattering Network. We are thus obtaining an invariant and thresholded representation of the signals providing significant performance gains compared to the non-thresholded version.
Keywords:
Upper bound
Scattering
Signal processing algorithms
Computational complexity
Discrete wavelet transforms
Bird song
classification
frame
overcomplete
scattering network
sparsity
thresholding
wavelet