Scale-invariant sparse PCA on high-dimensional meta-elliptical data

Fang Han, Han Liu

Research output: Contribution to journalArticle

24 Scopus citations

Abstract

We propose a semiparametric method for conducting scale-invariant sparse principal component analysis (PCA) on high-dimensional non-Gaussian data. Compared with sparse PCA, our method has a weaker modeling assumption and is more robust to possible data contamination. Theoretically, the proposed method achieves a parametric rate of convergence in estimating the parameter of interests under a flexible semiparametric distribution family; computationally, the proposed method exploits a rank-based procedure and is as efficient as sparse PCA; empirically, our method outperforms most competing methods on both synthetic and real-world datasets.

Original languageEnglish (US)
Pages (from-to)275-287
Number of pages13
JournalJournal of the American Statistical Association
Volume109
Issue number505
DOIs
StatePublished - 2014

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Elliptical distribution
  • High-dimensional statistics
  • Principal component analysis
  • Robust statistics

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