Multi-atlas based morphometric pattern analysis has been recently proposed for the automatic diagnosis of Alzheimer’s disease (AD) and its early stage, i.e., mild cognitive impairment (MCI), where multi-view feature representations for subjects are generated by using multiple atlases. However, existing multi-atlas based methods usually assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while the underlying distribution of data is actually a prior unknown. In this paper, we propose an inherent structure-guided multi-view leaning (ISML) method for AD/MCI classification. Specifically, we first extract multi-view features for subjects using multiple selected atlases, and then cluster subjects in the original classes into several sub-classes (i.e., clusters) in each atlas space. Then, we encode each subject with a new label vector, by considering both the original class labels and the coding vectors for those sub-classes, followed by a multi-task feature selection model in each of multi-atlas spaces. Finally, we learn multiple SVM classifiers based on the selected features, and fuse them together by an ensemble classification method. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrate that our method achieves better performance than several state-of-the-art methods in AD/MCI classification.