Classification of functional data: A segmentation approach
Document Type
Article
Publication Date
6-15-2008
Abstract
We suggest a classification and feature extraction method on functional data where the predictor variables are curves. The method, called functional segment discriminant analysis (FSDA), combines the classical linear discriminant analysis and support vector machine. FSDA is particularly useful for irregular functional data, characterized by spatial heterogeneity and local patterns like spikes. FSDA not only reduces the computation and storage burden by using a fraction of the spectrum, but also identifies important predictors and extracts features. FSDA is highly flexible, easy to incorporate information from other data sources and/or prior knowledge from the investigators. We apply FSDA to two public domain data sets and discuss the understanding developed from the study. © 2008 Elsevier B.V. All rights reserved.
Publication Source (Journal or Book title)
Computational Statistics and Data Analysis
First Page
4790
Last Page
4800
Recommended Citation
Li, B., & Yu, Q. (2008). Classification of functional data: A segmentation approach. Computational Statistics and Data Analysis, 52 (10), 4790-4800. https://doi.org/10.1016/j.csda.2008.03.024