title: Scale-Invariant Sparse PCA on High Dimensional Meta-elliptical Data.
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 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.
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA; fhan@jhsph.edu.
authors.forename: Fang
authors.initials: F
authors.lastname: Han
authors.affiliation: Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544, USA; hanliu@princeton.edu .
authors.forename: Han
authors.initials: H
authors.lastname: Liu
keywords: Elliptical distribution; High dimensional statistics; Principal component analysis; Robust statistics