Special Session 129: Mathematics of Data Science and Applications

Decomposition of Electrodermal Activity Signals Using Matrix Separation
Xuemei Chen
University of North Carolina Wilmington
USA
Co-Author(s):    
Abstract:
We develop a new method for decomposing EDA signals using matrix decomposition. This matrix decomposition framework is new and we present some theoretical guarantees of recovery from a convex optimization problem. We further develop efficient algorithms for this convex optimization program where we also propose a preconditioning technique. Some numerical experiments with real data is also presented.