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
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio