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A communication-efficient and privacy-aware distributed algorithm for sparse PCA

Author

Listed:
  • Lei Wang

    (Academy of Mathematics and Systems Science
    University of Chinese Academy of Sciences)

  • Xin Liu

    (Academy of Mathematics and Systems Science
    University of Chinese Academy of Sciences)

  • Yin Zhang

    (The Chinese University of Hong Kong)

Abstract

Sparse principal component analysis (PCA) improves interpretability of the classic PCA by introducing sparsity into the dimension-reduction process. Optimization models for sparse PCA, however, are generally non-convex, non-smooth and more difficult to solve, especially on large-scale datasets requiring distributed computation over a wide network. In this paper, we develop a distributed and centralized algorithm called DSSAL1 for sparse PCA that aims to achieve low communication overheads by adapting a newly proposed subspace-splitting strategy to accelerate convergence. Theoretically, convergence to stationary points is established for DSSAL1. Extensive numerical results show that DSSAL1 requires far fewer rounds of communication than state-of-the-art peer methods. In addition, we make the case that since messages exchanged in DSSAL1 are well-masked, the possibility of private-data leakage in DSSAL1 is much lower than in some other distributed algorithms.

Suggested Citation

  • Lei Wang & Xin Liu & Yin Zhang, 2023. "A communication-efficient and privacy-aware distributed algorithm for sparse PCA," Computational Optimization and Applications, Springer, vol. 85(3), pages 1033-1072, July.
    • Handle: RePEc:spr:coopap:v:85:y:2023:i:3:d:10.1007_s10589-023-00481-4
    DOI: 10.1007/s10589-023-00481-4
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    References listed on IDEAS

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