Adaptive support driven Bayesian reweighted algorithm for sparse signal recovery

TL;DR

This paper introduces an adaptive, support-driven Bayesian reweighted algorithm that efficiently recovers sparse signals from large datasets, reducing computational costs while accurately identifying key features.

Contribution

It proposes a novel adaptive support estimation method with a restart strategy, improving efficiency and accuracy over existing sparse recovery algorithms.

Findings

Outperforms state-of-the-art methods in numerical experiments

Reduces computation and memory requirements

Effectively extracts major features from large datasets

Abstract

Sparse learning has been widely studied to capture critical information from enormous data sources in the filed of system identification. Often, it is essential to understand internal working mechanisms of unknown systems (e.g. biological networks) in addition to input-output relationships. For this purpose, various feature selection techniques have been developed. For example, sparse Bayesian learning (SBL) was proposed to learn major features from a dictionary of basis functions, which makes identified models interpretable. Reweighted L1-regularization algorithms are often applied in SBL to solve optimization problems. However, they are expensive in both computation and memory aspects, thus not suitable for large-scale problems. This paper proposes an adaptive support driven Bayesian reweighted (ASDBR) algorithm for sparse signal recovery. A restart strategy based on shrinkage-thresholding is developed to conduct adaptive support estimate, which can effectively reduce computation burden and memory demands. Moreover, ASDBR accurately extracts major features and excludes redundant information from large datasets. Numerical experiments demonstrate the proposed algorithm outperforms state-of-the-art methods.