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Convergence Acceleration for Multiobjective Sparse Reconstruction via Knowledge Transfer

  • Bai YanEmail author
  • Qi Zhao
  • J. Andrew Zhang
  • Yonghui Li
  • Zhihai Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11411)

Abstract

Multiobjective sparse reconstruction (MOSR) methods can potentially obtain superior reconstruction performance. However, they suffer from high computational cost, especially in high-dimensional reconstruction. Furthermore, they are generally implemented independently without reusing prior knowledge from past experiences, leading to unnecessary computational consumption due to the re-exploration of similar search spaces. To address these problems, we propose a sparse-constraint knowledge transfer operator to accelerate the convergence of MOSR solvers by reusing the knowledge from past problem-solving experiences. Firstly, we introduce the deep nonlinear feature coding method to extract the feature mapping between the search of the current problem and a previously solved MOSR problem. Through this mapping, we learn a set of knowledge-induced solutions which contain the search experience of the past problem. Thereafter, we develop and apply a sparse-constraint strategy to refine these learned solutions to guarantee their sparse characteristics. Finally, we inject the refined solutions into the iteration of the current problem to facilitate the convergence. To validate the efficiency of the proposed operator, comprehensive studies on extensive simulated signal reconstruction are conducted.

Keywords

Sparse reconstruction Multiobjective evolutionary algorithm Learning Knowledge transfer 

Notes

Acknowledgments

This work was supported by the China Scholarship Council under Grant 201706540025.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bai Yan
    • 1
    Email author
  • Qi Zhao
    • 2
  • J. Andrew Zhang
    • 3
  • Yonghui Li
    • 4
  • Zhihai Wang
    • 5
  1. 1.Institute of Laser EngineeringBeijing University of TechnologyBeijingChina
  2. 2.College of Economics and ManagementBeijing University of TechnologyBeijingChina
  3. 3.Global Big Data Technologies CentreUniversity of Technology SydneySydneyAustralia
  4. 4.School of Electrical and Information EngineeringUniversity of SydneySydneyAustralia
  5. 5.Key Laboratory of Optoelectronics Technology, Ministry of EducationBeijing University of TechnologyBeijingChina

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