Abstract Number:
2115
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Shuoyang Wang (1), guanqun Cao (2)
Institutions:
(1) Yale University, N/A, (2) Michigan State University, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
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.
Keywords:
functional data analysis|deep neural network|feature selection|classification| |
Sponsors:
IMS
Tracks:
Statistical Methodology
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