Linear Convergence of a Proximal Alternating Minimization Method with Extrapolation for ℓ1-Norm Principal Component Analysis
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AbstractA popular robust alternative of the classic principal component analysis (PCA) is the ℓ1-norm PCA (L1-PCA), which aims to find a subspace that captures the most variation in a dataset as measured by the ℓ1-norm. L1-PCA has shown great promise in alleviating the effect of outliers in data analytic applications. However, it gives rise to a challenging nonsmooth, nonconvex optimization problem, for which existing algorithms are either not scalable or lack strong theoretical guarantees on their convergence behavior. In this paper, we propose a proximal alternating minimization method with extrapolation (PAMe) for solving a two-block reformulation of the L1-PCA problem. We then show that for both the L1-PCA problem and its two-block reformulation, the Kurdyka–Łojasiewicz exponent at any of the limiting critical points is 1/2. This allows us to establish the linear convergence of the sequence of iterates generated by PAMe and to determine the criticality of the limit of the sequence with respect to both the L1-PCA problem and its two-block reformulation. To complement our theoretical development, we show via numerical experiments on both synthetic and real-world datasets that PAMe is competitive with a host of existing methods. Our results not only significantly advance the convergence theory of iterative methods for L1-PCA but also demonstrate the potential of our proposed method in applications.
All Author(s) ListPeng Wang, Huikang Liu, Anthony Man-Cho So
Journal nameSIAM Journal on Optimization
Year2023
Month6
Volume Number33
Issue Number2
PublisherSociety for Industrial and Applied Mathematics Publications
Pages684 - 712
ISSN1052-6234
eISSN1095-7189
LanguagesEnglish-United States
