A novel support vector classifier for longitudinal high‐dimensional data and its application to neuroimaging data

摘要

Abstract Recent technological advances have made it possible for many studies to collect high dimensional data (HDD) longitudinally, for example, images collected during different scanning sessions. Such studies may yield temporal changes of selected features that, when incorporated with machine learning methods, are able to predict disease status or responses to a therapeutic treatment. Support vector machine (SVM) techniques are robust and effective tools well suited for the classification an.