A matching pursuit generalized approximate message passing algorithm

Yongjie Luo, Qun Wan, Guan Gui, Fumiyuki Adachi

    Research output: Contribution to journalArticlepeer-review

    5 Citations (Scopus)


    This paper proposes a novel matching pursuit generalized approximate message passing (MPGAMP) algorithm which explores the support of sparse representation coefficients step by step, and estimates the mean and variance of non-zero elements at each step based on a generalized-approximate-message-passing-like scheme. In contrast to the classic message passing based algorithms and matching pursuit based algorithms, our proposed algorithm saves a lot of intermediate process memory, and does not calculate the inverse matrix. Numerical experiments show that MPGAMP algorithm can recover a sparse signal from compressed sensing measurements very well, and maintain good performance even for non-zero mean projection matrix and strong correlated projection matrix.

    Original languageEnglish
    Pages (from-to)2723-2727
    Number of pages5
    JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
    Issue number12
    Publication statusPublished - 2015 Dec


    • Compressed sensing
    • Generalized approximate message passing
    • Matching pursuit
    • Robust

    ASJC Scopus subject areas

    • Signal Processing
    • Computer Graphics and Computer-Aided Design
    • Electrical and Electronic Engineering
    • Applied Mathematics


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