Learning Optimal Shrinkage Splines for ADMM Algorithms
Ha Nguyen, EPFL STI LIB

Meeting • 22 November 2016 • BM 4 233

Abstract
I'll talk about a learning approach to signal denoising in which the shrinkage function in the ADMM algorithm is parameterized by coefficients of a polynomial spline. The spline coefficients are learned through a gradient descent to minimize the mean square error between a collection of ground-truth signals and their reconstructions from noisy data. We also propose to impose various constraints on the shrinkage function based on theoretical observations. These constraints are translated nicely into linear constraints on the spline coefficients, which results in a simple learning algorithm using projected gradient descent. Experiments show that denoising with learned shrinkage splines are optimal for various types of signals, either sparse or non-sparse.