In our present society, Alzheimer's disease (AD) is the most common dementia form in elderly people and has been a big social health problem worldwide. In this paper, we propose a novel multi-view classification method based on l (2,p) -norm regularization for Alzheimer's Disease (AD) diagnosis. Unlike the previous l (2,1) -norm regularized methods using concatenated multi-view features, we further consider the intra-structure and inter-structure relations between features of different views and use a more flexible l (2,p) -norm regularization in our objective function. We also proposed a more suitable loss function to measure the loss between labels and predicted values for classification task. It experimentally demonstrated that this method enhances the performance of disease status classification, comparing to the state-of-the-art methods.