Abstract: The development of image genetics has greatly promoted the research of mental diseases. It mainly analyzes and mines multimodal data to find out the disease-related pathogenesis. However, the data usually show the characteristics of group correlation or multiple feature correlation. It is difficult to find the relevant disease mechanism by traditional methods, which is prone to the problem of too sparse. To solve the above problems, this paper introduces the regularization term l1,2 norm which can achieve intra-group sparsity and inter-group smoothing, and jointly punishes canonical correlation analysis with the l2,1 norm which can achieve inter-group sparsity and intra-group smoothing. By optimizing the correlation between data, the feature selection of two-modal data sets with related group features and intra-group features is realized. The results of simulation experiments show that this method can not only accurately estimate the correlation coefficient between the two groups of data, but also select the relevant inter-group and intra-group features. On the real schizophrenia data set, this method can find more susceptibility genes and risk brain regions related to schizophrenia.

Key words: l1,2 norm, l2,1 norm, relevance, group sparse canonical correlation analysis, feature selection