2016 COLT COLT 2016

On the Approximability of Sparse PCA

Abstract

It is well known that Sparse PCA (Sparse Principal Component Analysis) is NP-hard to solve exactly on worst-case instances. What is the complexity of solving Sparse PCA approximately? Our contributions include: \beginenumerate \item a simple and efficient algorithm that achieves an n^-1/3-approximation; \item NP-hardness of approximation to within (1-\varepsilon), for some small constant \varepsilon > 0; \item SSE-hardness of approximation to within \em any constant factor; and \item an \exp\exp\left(Ω\left(\sqrt\log \log n\right)\right) (“quasi-quasi-polynomial”) gap for the standard semidefinite program. \endenumerate

🌉 Interdisciplinary Bridge — Machine Learning and Mathematics & Optimization
🐣 Hot Topic Early Bird — computational complexity
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