64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

Multiclass classification for multidimensional functional data through deep neural networks


64th ISI World Statistics Congress - Ottawa, Canada

Format: IPS Abstract

Keywords: classification, deep_learning, functional data analysis

Session: IPS 268 - Functional and High-dimensional Data Analysis: New Directions and Innovations

Monday 17 July 2 p.m. - 3:40 p.m. (Canada/Eastern)


The intrinsically infinite dimension features of the functional observations over multidimensional domains render the standard classification methods effectively inapplicable. To address this problem, we introduce a novel multiclass functional deep neural network (mfDNN) classifier as an innovative data mining and classification tool. Specifically, we consider sparse deep neural network architecture with ReLU activation function and minimize the cross-entropy loss in the multiclass classification setup. This neural network architecture allows us to employ modern computational tools in the implementation. The convergence rates of the misclassification risk functions are also derived for both fully observed and discretely observed multidimensional functional data. We demonstrate the performance of mfDNN on simulated data and several benchmark datasets from different application domains.