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

Author(s): Han, Fang; Liu, Han

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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.
Publication Date: 2014
Citation: Han, Fang, and Han Liu. "Scale-invariant sparse PCA on high-dimensional meta-elliptical data." Journal of the American Statistical Association 109, no. 505 (2014): 275-287. doi:10.1080/01621459.2013.844699
DOI: doi:10.1080/01621459.2013.844699
ISSN: 0162-1459
EISSN: 1537-274X
Pages: 275 - 287
Type of Material: Journal Article
Journal/Proceeding Title: Journal of the American Statistical Association
Version: Author's manuscript

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