Nearly optimal stochastic approximation for online principal subspace estimation

Xin Liang, Zhen Chen Guo, Li Wang, Ren Cang Li*, Wen Wei Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Principal component analysis (PCA) has been widely used in analyzing high-dimensional data. It converts a set of observed data points of possibly correlated variables into a set of linearly uncorrelated variables via an orthogonal transformation. To handle streaming data and reduce the complexities of PCA, (subspace) online PCA iterations were proposed to iteratively update the orthogonal transformation by taking one observed data point at a time. Existing works on the convergence of (subspace) online PCA iterations mostly focus on the case where the samples are almost surely uniformly bounded. In this paper, we analyze the convergence of a subspace online PCA iteration under more practical assumption and obtain a nearly optimal finite-sample error bound. Our convergence rate almost matches the minimax information lower bound. We prove that the convergence is nearly global in the sense that the subspace online PCA iteration is convergent with high probability for random initial guesses. This work also leads to a simpler proof of the recent work on analyzing online PCA for the first principal component only.

Original languageEnglish
Pages (from-to)1087-1122
Number of pages36
JournalScience China Mathematics
Issue number5
StatePublished - May 2023


  • 62H12
  • 62H25
  • 65F99
  • 68W27
  • finite-sample analysis
  • high-dimensional data
  • online algorithm
  • principal component analysis
  • principal component subspace
  • stochastic approximation


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