Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We show how this method can easily be extended to a setting where the data has a hierarchical multi-view structure. We apply StaPLR to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.

翻译：多视图数据是指将特征分为特征集的设置,例如,由于这些特征与不同来源相对应。堆积式受限后勤回归(StaPLR)是最近采用的一种方法,可用于分类和自动选择对预测最重要的观点。我们表明这种方法如何很容易推广到数据具有分级多视图结构的设置。我们将StaPLR应用于阿尔茨海默氏病分类,从以下三种扫描类型计算出不同的MRI措施:结构MRI、扩散加权MRI和休息状态FMRI。SAPLR可以确定哪些扫描类型和哪些MRI措施对分类最为重要,在分类性能方面表现优于弹性净回归。