Generalized Thresholding and Online Sparsity-Aware Learning in a Union of Subspaces

TL;DR

This paper introduces a unified generalized thresholding operator and an online learning algorithm for sparse signal recovery in dynamic environments, with theoretical convergence guarantees and competitive empirical performance.

Contribution

It presents a novel generalized thresholding operator, an online sparsity-promoting learning scheme (APGT), and a new analytical framework using partially quasi-nonexpansive mappings.

Findings

APGT shows competitive performance against more complex methods.

Theoretical convergence of APGT is established.

The GT operator unifies many sparsity-promoting thresholding rules.

Abstract

This paper studies a sparse signal recovery task in time-varying (time-adaptive) environments. The contribution of the paper to sparsity-aware online learning is threefold; first, a Generalized Thresholding (GT) operator, which relates to both convex and non-convex penalty functions, is introduced. This operator embodies, in a unified way, the majority of well-known thresholding rules which promote sparsity. Second, a non-convexly constrained, sparsity-promoting, online learning scheme, namely the Adaptive Projection-based Generalized Thresholding (APGT), is developed that incorporates the GT operator with a computational complexity that scales linearly to the number of unknowns. Third, the novel family of partially quasi-nonexpansive mappings is introduced as a functional analytic tool for treating the GT operator. By building upon the rich fixed point theory, the previous class of mappings helps us, also, to establish a link between the GT operator and a union of linear subspaces; a non-convex object which lies at the heart of any sparsity promoting technique, batch or online. Based on such a functional analytic framework, a convergence analysis of the APGT is provided. Furthermore, extensive experiments suggest that the APGT exhibits competitive performance when compared to computationally more demanding alternatives, such as the sparsity-promoting Affine Projection Algorithm (APA)- and Recursive Least Squares (RLS)-based techniques.