Simultaneous Classification and Feature Selection for Complex Functional Data

Oregon Convention Center 

Classification using high-dimensional features arises frequently in many contemporary statistical studies such as imaging data classification for PET scan or other high-throughput data. The difficulty of high-dimensional functional data classification is intrinsically caused by the existence of many noise features that do not contribute to the reduction of misclassification rate. There is limited study on the analysis of the impacts of high dimensionality on functional data classification. We bridge the gap by proposing a deep neural network-based algorithms which perform penalized classification and feature selection simultaneously. Simulation studies and real data analysis support our theoretical results and demonstrate convincingly the advantage of our new classification procedure.

functional data analysis

deep neural network

feature selection

classification 

IMS