B0299
Title: Optimal classification for functional data
Authors: Guanqun Cao - Auburn University (United States) [presenting]
Abstract: A central topic in functional data analysis is how to design an optimal decision rule, based on training samples, to classify a data function. We exploit the optimal classification problem when data functions are Gaussian processes. Sharp nonasymptotic convergence rates for minimax excess misclassification risk are derived in both settings that data functions are fully observed and discretely observed. We explore two easily implementable classifiers based on discriminant analysis and deep neural network, respectively, which are both proven to achieve optimality in a Gaussian setting. Our deep neural network classifier is new in literature, demonstrating outstanding performance even when data functions are non-Gaussian. In the case of discretely observed data, we discover a novel critical sampling frequency that governs the sharp convergence rates. The proposed classifiers perform favorably in finite-sample applications, as we demonstrate through comparisons with other functional classifiers in simulations and one real data application.
