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Consistency of sparse PCA in High Dimension, Low Sample Size contexts

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  • Shen, Dan
  • Shen, Haipeng
  • Marron, J.S.

Abstract

Sparse Principal Component Analysis (PCA) methods are efficient tools to reduce the dimension (or number of variables) of complex data. Sparse principal components (PCs) are easier to interpret than conventional PCs, because most loadings are zero. We study the asymptotic properties of these sparse PC directions for scenarios with fixed sample size and increasing dimension (i.e. High Dimension, Low Sample Size (HDLSS)). We consider the previously studied single spike covariance model and assume in addition that the maximal eigenvector is sparse. We extend the existing HDLSS asymptotic consistency and strong inconsistency results of conventional PCA in an entirely new direction. We find a large set of sparsity assumptions under which sparse PCA is still consistent even when conventional PCA is strongly inconsistent. The consistency of sparse PCA is characterized along with rates of convergence. Furthermore, we clearly identify the mathematical boundaries of the sparse PCA consistency, by showing strong inconsistency for an oracle version of sparse PCA beyond the consistent region, as well as its inconsistency on the boundaries of the consistent region. Simulation studies are performed to validate the asymptotic results in finite samples.

Suggested Citation

  • Shen, Dan & Shen, Haipeng & Marron, J.S., 2013. "Consistency of sparse PCA in High Dimension, Low Sample Size contexts," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 317-333.
    • Handle: RePEc:eee:jmvana:v:115:y:2013:i:c:p:317-333
    DOI: 10.1016/j.jmva.2012.10.007
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    References listed on IDEAS

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    1. Peter Hall & J. S. Marron & Amnon Neeman, 2005. "Geometric representation of high dimension, low sample size data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 427-444, June.
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    7. Mihee Lee & Haipeng Shen & Jianhua Z. Huang & J. S. Marron, 2010. "Biclustering via Sparse Singular Value Decomposition," Biometrics, The International Biometric Society, vol. 66(4), pages 1087-1095, December.
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    3. Jianqing Fan & Yuan Liao & Martina Mincheva, 2013. "Large covariance estimation by thresholding principal orthogonal complements," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(4), pages 603-680, September.
    4. Li, Gen & Yang, Dan & Nobel, Andrew B. & Shen, Haipeng, 2016. "Supervised singular value decomposition and its asymptotic properties," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 7-17.
    5. Kim, Donggyu & Wang, Yazhen, 2016. "Sparse PCA-based on high-dimensional Itô processes with measurement errors," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 172-189.
    6. Xinyi Zhong & Chang Su & Zhou Fan, 2022. "Empirical Bayes PCA in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 853-878, July.
    7. Fang, Kuangnan & Fan, Xinyan & Zhang, Qingzhao & Ma, Shuangge, 2018. "Integrative sparse principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 1-16.
    8. Aaron Fisher & Brian Caffo & Brian Schwartz & Vadim Zipunnikov, 2016. "Fast, Exact Bootstrap Principal Component Analysis for > 1 Million," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 846-860, April.

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