Multi-task learning aims at combining information across tasks to boost prediction performance, especially when the number of training samples is small and the number of predictors is very large. In this paper, we first extend the Sparse Discriminate Analysis (SDA) of Clemmensen et al.. We call this Multi-task Sparse Discriminate Analysis (MtSDA). MtSDA formulates multi-label prediction as a quadratic optimization problem whereas SDA obtains single labels via a nearest class mean rule. Second, we propose a class of equicorrelation matrices to use in MtSDA which includes the identity matrix. MtSDA with both matrices are compared with singletask learning (SVM and LDA+SVM) and multi-task learning (HSML). The comparisons are made on real data sets in terms of AUC and F-measure. The data results show that MtSDA outperforms other methods substantially almost all the time and in some cases MtSDA with the equicorrelation matrix substantially outperforms MtSDA with identity matrix.