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Probability-enhanced effective dimension reduction for classifying sparse functional data

Author

Listed:
  • Fang Yao

    (University of Toronto)

  • Yichao Wu

    (North Carolina State University)

  • Jialin Zou

    (University of Toronto)

Abstract

We consider the classification of sparse functional data that are often encountered in longitudinal studies and other scientific experiments. To utilize the information from not only the functional trajectories but also the observed class labels, we propose a probability-enhanced method achieved by weighted support vector machine based on its Fisher consistency property to estimate the effective dimension reduction space. Since only a few measurements are available for some, even all, individuals, a cumulative slicing approach is suggested to borrow information across individuals. We provide justification for validity of the probability-based effective dimension reduction space, and a straightforward implementation that yields a low-dimensional projection space ready for applying standard classifiers. The empirical performance is illustrated through simulated and real examples, particularly in contrast to classification results based on the prominent functional principal component analysis.

Suggested Citation

  • Fang Yao & Yichao Wu & Jialin Zou, 2016. "Probability-enhanced effective dimension reduction for classifying sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 1-22, March.
    • Handle: RePEc:spr:testjl:v:25:y:2016:i:1:d:10.1007_s11749-015-0470-2
    DOI: 10.1007/s11749-015-0470-2
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    5. Alvarez, Agustín & Boente, Graciela & Kudraszow, Nadia, 2019. "Robust sieve estimators for functional canonical correlation analysis," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 46-62.

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