Functional data classification: a wavelet approach

Chung Chang (), Yakuan Chen and R. Ogden

Computational Statistics, 2014, vol. 29, issue 6, 1497-1513

Abstract: In recent years, several methods have been proposed to deal with functional data classification problems (e.g., one-dimensional curves or two- or three-dimensional images). One popular general approach is based on the kernel-based method, proposed by Ferraty and Vieu (Comput Stat Data Anal 44:161–173, 2003 ). The performance of this general method depends heavily on the choice of the semi-metric. Motivated by Fan and Lin (J Am Stat Assoc 93:1007–1021, 1998 ) and our image data, we propose a new semi-metric, based on wavelet thresholding for classifying functional data. This wavelet-thresholding semi-metric is able to adapt to the smoothness of the data and provides for particularly good classification when data features are localized and/or sparse. We conduct simulation studies to compare our proposed method with several functional classification methods and study the relative performance of the methods for classifying positron emission tomography images. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Wavelet thresholding; Semi-metric; Functional data classification; Sparse; Kernel (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s00180-014-0503-4

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