High Dimensional Semiparametric Scale-Invariant Principal Component Analysis

Fang Han, Han Liu

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Copula Component Analysis (COCA). The semiparametric model assumes that, after unspecified marginally monotone transformations, the distributions are multivariate Gaussian. COCA improves upon PCA and sparse PCA in three aspects: (i) It is robust to modeling assumptions; (ii) It is robust to outliers and data contamination; (iii) It is scale-invariant and yields more interpretable results. We prove that the COCA estimators obtain fast estimation rates and are feature selection consistent when the dimension is nearly exponentially large relative to the sample size. Careful experiments confirm that COCA outperforms sparse PCA on both synthetic and real-world data sets.

Original languageEnglish (US)
Article number6747357
Pages (from-to)2016-2032
Number of pages17
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number10
StatePublished - Oct 1 2014


  • High dimensional statistics
  • Nonparanormal distribution
  • Principal component analysis
  • Robust statistics

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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