  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.
