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

Lei Wang (), Xin Liu () and Yin Zhang ()
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Lei Wang: Academy of Mathematics and Systems Science
Xin Liu: Academy of Mathematics and Systems Science
Yin Zhang: The Chinese University of Hong Kong

Computational Optimization and Applications, 2023, vol. 85, issue 3, No 12, 1033-1072

Abstract: 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.

Keywords: Alternating direction method of multipliers; Distributed computing; Optimization with orthogonality constraints; Sparse PCA (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10589-023-00481-4

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