Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Estimation in generalized linear models for functional data via penalized likelihood
Journal of Multivariate Analysis
Factor-based comparison of groups of curves
Computational Statistics & Data Analysis
Robust estimation and classification for functional data via projection-based depth notions
Computational Statistics
Computational Statistics & Data Analysis
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Classification problems involving a categorical class label Y and a functional predictor X(t) are becoming increasingly common. Since X(t) is infinite dimensional, some form of dimension reduction is essential in these problems. Conventional dimension reduction techniques for functional data usually suffer from one or both of the following problems. First, they do not take the categorical response into consideration, and second, the resulting reduced subspace may have a complicated relationship with the original functional data. In this paper we propose a dimension reduction method, ''Functional Adaptive Classification'' (FAC), specifically designed for functional classification problems. FAC uses certain complexity constraints to ensure that the reduced subspace has an easily interpretable relationship to the original functional predictor. Extensive simulation studies and an fMRI (functional Magnetic Resonance Imaging) study show that FAC is extremely competitive in comparison to other potential approaches in terms of both classification accuracy and model interpretability.