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Dimension reduction in principal component analysis for trees

Author

Listed:
  • Alfaro, Carlos A.
  • Aydın, Burcu
  • Valencia, Carlos E.
  • Bullitt, Elizabeth
  • Ladha, Alim

Abstract

The statistical analysis of tree structured data is a new topic in statistics with wide application areas. Some Principal Component Analysis (PCA) ideas have been previously developed for binary tree spaces. These ideas are extended to the more general space of rooted and ordered trees. Concepts such as tree-line and forward principal component tree-line are redefined for this more general space, and the optimal algorithm that finds them is generalized.

Suggested Citation

  • Alfaro, Carlos A. & Aydın, Burcu & Valencia, Carlos E. & Bullitt, Elizabeth & Ladha, Alim, 2014. "Dimension reduction in principal component analysis for trees," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 157-179.
    • Handle: RePEc:eee:csdana:v:74:y:2014:i:c:p:157-179
    DOI: 10.1016/j.csda.2013.12.007
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    References listed on IDEAS

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    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
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