Near-Optimal Stochastic Approximation for Online Principal Component Estimation

Understand

Principal component analysis (PCA) has been a prominent tool for high-dimensional data analysis.

• Online algorithms that estimate the principal component by processing streaming data are of tremendous practical and theoretical interests.
• Despite its rich applications, theoretical convergence analysis remains largely open.
• In this paper, we cast online PCA into a stochastic nonconvex optimization problem, and we analyze the online PCA algorithm as a stochastic approximation iteration.