  In recent years, there are many research cases for the diagnosis of
Parkinson's disease (PD) with the brain magnetic resonance imaging (MRI) by
utilizing the traditional unsupervised machine learning methods and the
supervised deep learning models. However, unsupervised learning methods are not
good at extracting accurate features among MRIs and it is difficult to collect
enough data in the field of PD to satisfy the need of training deep learning
models. Moreover, most of the existing studies are based on single-view MRI
data, of which data characteristics are not sufficient enough. In this paper,
therefore, in order to tackle the drawbacks mentioned above, we propose a novel
semi-supervised learning framework called Semi-supervised Multi-view learning
Clustering architecture technology (SMC). The model firstly introduces the
sliding window method to grasp different features, and then uses the
dimensionality reduction algorithms of Linear Discriminant Analysis (LDA) to
process the data with different features. Finally, the traditional single-view
clustering and multi-view clustering methods are employed on multiple feature
views to obtain the results. Experiments show that our proposed method is
superior to the state-of-art unsupervised learning models on the clustering
effect. As a result, it may be noted that, our work could contribute to
improving the effectiveness of identifying PD by previous labeled and
subsequent unlabeled medical MRI data in the realistic medical environment.
