Multi-modal neural image feature selection method based on sample weight and low-rank constraint

The invention discloses a multi-modal neural image feature selection method based on sample weight and low-rank constraint. The method comprises the following steps: (1) collecting multi-modal brain image data; (2) after data of multiple modals are obtained, feature selection being carried out in a multi-modal feature collaborative analysis mode, a regression model from each modal datum to a classification class label being established, group sparse constraint being carried out on regression vectors, and therefore a common feature subset related to all tasks being obtained; (3) multi-modal data feature modeling; (4) writing the target function into an augmented Lagrange form, and changing the target function into a convex function; (5) obtaining the weight matrix of each mode of each sample and the weight of each feature; and (6) using a multi-kernel support vector machine method to fuse the sample multi-modal features for classification. According to the technical scheme, the importance of samples among modals and the correlation among the modals are considered, more meaningful features are found out, and the classification and prediction accuracy is improved.