In the field of medical imaging analysis using machine learning, the challenge is lack of training sample. In order to solve the problem, a weight-distribution based Lasso (Least absolute shrinkage and selection operator) feature learning model is proposed and applied to early diagnosis of Alzheimer's Disease (AD). Specifically, the proposed diagnosis method is consisted of two components:weight-distribution based Lasso feature selection (WDL) and large margin distribution machine (LDM) for classification. Firstly, in order to capture data distribution information among multimodal features, the WDL feature selection model was built, to improve on the conventional Lasso model via adding a regularization item of weight-distribution. Secondly, in order to achieve better generalization and accuracy on classification, and also to keep complementary information among multimodal features, the LDM algorithm is used for the training of the classifier. To evaluate the effectiveness of the proposed learning model, 202 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with multimodal features were employed. Experimental results on the ADNI database show that it can recognize AD from Normal Controls (NC) with 97.5% accuracy, recognize Mild Cognitive Impairment (MCI) from NC with 83.1% accuracy, and recognize progressive MCI (pMCI) patients from stable MCI (sMCI) ones with 84.8% accuracy, which demonstrate that it can significantly improve the performance of early AD diagnosis and achieve feature ranking in terms of discrimination via optimized weight vector.