Scale-Invariant Sparse PCA on High-Dimensional Meta-Elliptical Data

Fang Han and Han Liu

Journal of the American Statistical Association, 2014, vol. 109, issue 505, 275-287

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.

Date: 2014
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Citations: View citations in EconPapers (10)

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DOI: 10.1080/01621459.2013.844699

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