Matching Pursuit LASSO Part II: Applications and Sparse Recovery over Batch Signals
This work addresses sparse recovery problems in domains like compressive sensing and face recognition, offering significant speed improvements for batch processing, though it is incremental as it builds on prior MPL work.
The paper tackles the challenge of batch sparse recovery for many signals simultaneously, developing a batch-mode Matching Pursuit LASSO (BMPL) algorithm that improves efficiency, achieving up to 400 times faster performance than existing state-of-the-art methods in compressive sensing and face recognition tasks.
Matching Pursuit LASSIn Part I \cite{TanPMLPart1}, a Matching Pursuit LASSO ({MPL}) algorithm has been presented for solving large-scale sparse recovery (SR) problems. In this paper, we present a subspace search to further improve the performance of MPL, and then continue to address another major challenge of SR -- batch SR with many signals, a consideration which is absent from most of previous $\ell_1$-norm methods. As a result, a batch-mode {MPL} is developed to vastly speed up sparse recovery of many signals simultaneously. Comprehensive numerical experiments on compressive sensing and face recognition tasks demonstrate the superior performance of MPL and BMPL over other methods considered in this paper, in terms of sparse recovery ability and efficiency. In particular, BMPL is up to 400 times faster than existing $\ell_1$-norm methods considered to be state-of-the-art.O Part II: Applications and Sparse Recovery over Batch Signals