• DOI:10.1007/s10589-023-00481-4
• Corpus ID: 235358663

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

@article{Wang2021ACA,
  title={A communication-efficient and privacy-aware distributed algorithm for sparse PCA},
  author={Lei Wang and Xin Qi Liu and Yin Zhang},
  journal={Computational Optimization and Applications},
  year={2021},
  pages={1-40}
}

• Lei Wang, X. Liu, Yin Zhang
• Published 7 June 2021
• Computer Science
• Computational Optimization and Applications

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.