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arXiv · 1512.04754

Learning optimal nonlinearities for iterative thresholding algorithms

Ulugbek S. Kamilov·Hassan Mansour
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Abstract

Iterative shrinkage/thresholding algorithm (ISTA) is a well-studied method for finding sparse solutions to ill-posed inverse problems. In this letter, we present a data-driven scheme for learning optimal thresholding functions for ISTA. The proposed scheme is obtained by relating iterations of ISTA to layers of a simple deep neural network (DNN) and developing a corresponding error backpropagation algorithm that allows to fine-tune the thresholding functions. Simulations on sparse statistical signals illustrate potential gains in estimation quality due to the proposed data adaptive ISTA.

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BibTeXRIS

Ulugbek S. Kamilov, Hassan Mansour. 2015-12-15. Learning optimal nonlinearities for iterative thresholding algorithms. https://doi.org/10.1109/lsp.2016.2548245

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