Machine learning has been used in the past for the auxiliary diagnosis of Alzheimer’s Disease (AD). However, most existing technologies only explore single-view data, require manual parameter setting and focus on two-class (i.e., dementia or not) classification problems. Unlike single-view data, multi-view data provide more powerful feature representation capability. Learning with multi-view data is referred to as multi-view learning, which has received certain attention in recent years. In this paper, we propose a new multi-view clustering model called Consensus Multi-view Clustering (CMC) based on nonnegative matrix factorization for predicting the multiple stages of AD progression. The proposed CMC performs multi-view learning idea to fully capture data features with limited medical images, approaches similarity relations between different entities, addresses the shortcoming from multi-view fusion that requires manual setting parameters, and further acquires a consensus representation containing shared features and complementary knowledge of multiple view data. It not only can improve the predication performance of AD, but also can screen and classify the symptoms of different AD’s phases. Experimental results using data with twelve views constructed by brain Magnetic Resonance Imaging (MRI) database from Alzheimer’s Disease Neuroimaging Initiative expound and prove the effectiveness of the proposed model.

过去，机器学习已用于辅助诊断阿尔茨海默氏病（AD）。但是，大多数现有技术仅浏览单视图数据，需要手动设置参数并关注两类（即是否为痴呆）分类问题。与单视图数据不同，多视图数据提供了更强大的功能表示功能。使用多视图数据进行的学习称为多视图学习，近年来受到了一定的关注。在本文中，我们提出了一种基于非负矩阵分解的新的多视图聚类模型，称为共识多视图聚类（CMC），用于预测AD进展的多个阶段。建议的CMC执行多视图学习的想法，以完全捕获具有有限医学图像的数据特征，该方法解决了不同实体之间的相似关系，解决了需要手动设置参数的多视图融合的缺点，并进一步获得了包含共享特征和多视图数据的补充知识的共识表示。它不仅可以提高AD的预测性能，而且可以筛选和分类不同AD阶段的症状。实验结果使用由阿尔茨海默氏病神经影像学倡议组织的脑磁共振成像（MRI）数据库构建的十二个视图的数据进行了阐述，并证明了该模型的有效性。它不仅可以提高AD的预测性能，而且可以筛选和分类不同AD阶段的症状。实验结果使用由阿尔茨海默氏病神经影像学倡议组织的脑磁共振成像（MRI）数据库构建的十二个视图的数据进行了阐述，并证明了该模型的有效性。它不仅可以提高AD的预测性能，而且可以筛选和分类不同AD阶段的症状。实验结果使用由阿尔茨海默氏病神经影像学倡议组织的脑磁共振成像（MRI）数据库构建的十二个视图的数据进行了阐述，并证明了该模型的有效性。