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Perturbation theory for cross data matrix-based PCA

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  • Wang, Shao-Hsuan
  • Huang, Su-Yun

Abstract

Principal component analysis (PCA) has long been a useful and important tool for dimension reduction. However, this method must be used with care under certain circumstances such as high dimension and small sample size. In general, low dimension with large sample size or large signal to noise ratio is vital to guarantee the consistency of the leading eigenvalues and eigenvectors obtained by PCA. Cross data matrix (CDM)-based PCA is another way to estimate PCA components, through splitting data into two subsets and calculating singular value decomposition for the cross product of the corresponding covariance matrices. It has been shown that CDM-based PCA has a broader region of consistency than ordinary PCA for leading eigenvalues and eigenvectors. Although the difference in regions of consistency is well studied, an interesting practical as well as theoretical question is how they differ in eigenvalues and eigenvectors estimation, especially for the case where both fall in a common region of consistency. In this article, we derive the finite sample approximation results as well as the asymptotic behavior for CDM-based PCA via matrix perturbation. Furthermore, we also derive a comparison measure for CDM-based PCA vs. ordinary PCA. This measure only depends on the data dimension, noise correlations and the noise-to-signal ratio (NSR). Using this measure, we develop an algorithm, which selects good partitions and integrates results from these good partitions to form a final estimate for CDM-based PCA. Numerical and real data examples are presented for illustration.

Suggested Citation

  • Wang, Shao-Hsuan & Huang, Su-Yun, 2022. "Perturbation theory for cross data matrix-based PCA," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
  • Handle: RePEc:eee:jmvana:v:190:y:2022:i:c:s0047259x22000082
    DOI: 10.1016/j.jmva.2022.104960
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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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    6. Yata, Kazuyoshi & Aoshima, Makoto, 2010. "Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2060-2077, October.
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