title: Label-noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study.
abstract: Alzheimer's disease (AD) is usually diagnosed by clinicians through cognitive and functional performance test with a potential risk of misdiagnosis. Since the progression of AD is known to cause structural changes in the corpus callosum (CC), the CC thickness can be used as a functional covariate in AD classification problem for a diagnosis. However, misclassified class labels negatively impact the classification performance. Motivated by AD-CC association studies, we propose a logistic regression for functional data classification that is robust to misdiagnosis or label noise. Specifically, our logistic regression model is constructed by adopting individual intercepts to functional logistic regression model. This approach enables to indicate which observations are possibly mislabeled and also lead to a robust and efficient classifier. An effective algorithm using MM algorithm provides simple closed-form update formulas. We test our method using synthetic datasets to demonstrate its superiority over an existing method, and apply it to differentiating patients with AD from healthy normals based on CC from MRI.
authors.affiliation: Department of Statistics, Hankuk University of Foreign Studies, Yongin, Gyeonggi, Korea.
authors.forename: Seokho
authors.initials: S
authors.lastname: Lee
authors.affiliation: Frontier CS Lab, Software R&D Center, Samsung Electronics, Seoul, Korea.
authors.forename: Hyejin
authors.initials: H
authors.lastname: Shin
authors.affiliation: The Nathan S. Kline Institute for Psychiatric Research, New York, U.S.A.
authors.forename: Sang Han
authors.initials: SH
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000145:classification / Q000175:diagnosis*; D003337:Corpus Callosum / Q000473:pathology; D006801:Humans; D016015:Logistic Models; D008279:Magnetic Resonance Imaging
keywords: Alzheimer's disease; Fisher consistency; Functional data classification; Label noise; MM algorithm; Outlier detection; Robust classification