Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer’s disease
随机非线性定义域上函数型数据的可解释判别分析：阿尔茨海默病应用
Abstract We introduce a novel framework for the classification of functional data supported on nonlinear, and possibly random, manifold domains. The motivating application is the identification of subjects with Alzheimer’s disease from their cortical surface geometry and associated cortical thickness map. The proposed model is based upon a reformulation of the classification problem as a regularized multivariate functional linear regression model. This allows us to adopt a…