Classification of functional data: A segmentation approach

Authors:
Bin Li;Qingzhao Yu
Affiliations:
Louisiana State University, 70803 Baton Rouge, LA, United States;Louisiana State University, 70803 Baton Rouge, LA, United States
Venue:
Computational Statistics & Data Analysis
Year:
2008

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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.