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Geometric classifiers for high-dimensional noisy data

Author

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  • Ishii, Aki
  • Yata, Kazuyoshi
  • Aoshima, Makoto

Abstract

We consider the quadratic classification for high-dimensional data under the strongly spiked eigenvalue (SSE) model. High-dimensional data contain much information, however, it also contains huge amount of noise. We detect the high-dimensional noise as a spiked eigenstructure of high-dimensional covariance matrices. In order to find the difference between two populations, we utilize a geometric feature of high-dimensional data. The classification analysis based on the geometric feature of high-dimensional data is called geometrical quadratic discriminant analysis (GQDA). We create new GQDA on the basis of the high-dimensional spiked eigenstructures. We precisely study the influence of the spiked eigenstructure on GQDA using several examples. In order to remove the spiked noise, we use a data transformation technique. We show that our proposed classifier has a consistency property with respect to the error rate of misclassifying an individual. By using computer simulation, we discuss the performance of the proposed classifier. Finally, we give several demonstrations of data analysis using a microarray data set.

Suggested Citation

  • Ishii, Aki & Yata, Kazuyoshi & Aoshima, Makoto, 2022. "Geometric classifiers for high-dimensional noisy data," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    • Handle: RePEc:eee:jmvana:v:188:y:2022:i:c:s0047259x21001287
    DOI: 10.1016/j.jmva.2021.104850
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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.
    2. Yugo Nakayama & Kazuyoshi Yata & Makoto Aoshima, 2020. "Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(5), pages 1257-1286, October.
    3. Makoto Aoshima & Kazuyoshi Yata, 2019. "Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(3), pages 473-503, June.
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    5. Makoto Aoshima & Kazuyoshi Yata, 2014. "A distance-based, misclassification rate adjusted classifier for multiclass, high-dimensional data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(5), pages 983-1010, October.
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    8. Watanabe, Hiroki & Hyodo, Masashi & Seo, Takashi & Pavlenko, Tatjana, 2015. "Asymptotic properties of the misclassification rates for Euclidean Distance Discriminant rule in high-dimensional data," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 234-244.
    9. Yata, Kazuyoshi & Aoshima, Makoto, 2012. "Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 193-215.
    10. 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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    12. Makoto Aoshima & Kazuyoshi Yata, 2019. "High-Dimensional Quadratic Classifiers in Non-sparse Settings," Methodology and Computing in Applied Probability, Springer, vol. 21(3), pages 663-682, September.
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