Theory, Algorithms, and Applications of Sparse Recovery

Submission Deadline: 31 October 2018

Submission Deadline: 31 October 2018

IEEE Access invites manuscript submissions in the area of Theory, Algorithms, and Applications of Sparse Recovery.

Sparse recovery is a fundamental problem in the fields of compressed sensing, signal de-noising, statistical model selection, and more. The key idea of sparse recovery lies in that a suitably high dimensional sparse signal can be inferred from very few linear observations. Recent years have witnessed a great development of the sparse recovery theory and fruitful applications in the general field of information processing, including communications channel estimation, dictionary leaning, data compression, optical imaging, machine learning etc. Extensions to the recovery of low-rank matrices and higher order tensors from incomplete linear information have also been developed, and remarkable achievements have been achieved.

This Special Section is devoted to both the current state-of-the-art advances and new theory, algorithms and applications of sparse recovery, with the goals to highlight new achievements and developments, and to feature outstanding open issues and promising new directions and extensions, on the theory, algorithms, and applications. Both survey papers and papers of original contributions that enhance the existing body of sparse recovery are also highly encouraged. The topics of interest include, but are not limited to:

  • Fundamental limit of sparse recovery algorithms
  • Sparse recovery with phase-less sampling matrices
  • Trade-off between sparse recovery effectiveness and efficiency
  • Greedy methods for phase-less sparse recovery
  • Design and optimization for deterministic sampling matrices
  • Theory/algorithm/applications of sparse signal recovery
  • Theory/algorithm/applications of low-rank matrix recovery
  • Theory /algorithm/applications of tensor recovery
  • Efficient hardware implementation of sparse recovery algorithms
  • Sparse recovery for machine learning problems

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor: Jinming Wen, University of Toronto, Canada

Guest Editors:

  1. Jian Wang, Fudan University, China
  2. Bo Li, Nuance Communication, Canada
  3. Xin Yuan, Nokia Bell Labs, USA
  4. Kezhi Li, Imperial College London, UK


Relevant IEEE Access Special Sections:

  1. Advances in Channel Coding for 5G and Beyond
  2. Trends, Perspectives and Prospects of Machine Learning Applied to Biomedical Systems in  Internet of Medical Things
  3. Smart Caching, Communications, Computing and Cybersecurity for Information-Centric


IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: jinming.wen@mail.mcgill.ca