Probability-enhanced effective dimension reduction for classifying sparse functional data

Fang Yao (), Yichao Wu and Jialin Zou
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Fang Yao: University of Toronto
Yichao Wu: North Carolina State University
Jialin Zou: University of Toronto

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2016, vol. 25, issue 1, No 1, 22 pages

Abstract: 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.

Keywords: Classification; Cumulative slicing; Effective dimension reduction; Sparse functional data; Weighted support vector machine; 62H30 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11749-015-0470-2

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