Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality
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Sparse principal component analysis (PCA) is a popular dimensionality reduction technique for obtaining principal components which are linear combinations of a small subset of the original features. Existing approaches cannot supply certifiably optimal principal components with more than p=100s covariates. By reformulating sparse PCA as a convex mixed-integer semidefinite optimization problem, we design a cutting-plane method which solves the problem to certifiable optimality at the scale of selecting k=10s covariates from p=300 variables, and provides small bound gaps at a larger scale. We also propose two convex relaxations and randomized rounding schemes that provide certifiably near-exact solutions within minutes for p=100s or hours for p=1,000s. Using real-world financial and medical datasets, we illustrate our approach's ability to derive interpretable principal components tractably at scale.
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