Abstract: Motivated by the recent success of visual representation and the problem of conventional approaches which were too correlated or too strict constraint，this paper proposed a framework of decorrelating mixed norm multi-task sparse learning representation to seek an effective and intermediate feature representation approach．By leveraging side information about multiple features to group all kinds of features，L2，1and L1 mixed-norm regularizations were used as penalty function，for L2，1-norm's feature sharing information among a feature group and L1-norm's sparsity and competition among unrelated feature group． This framework simultaneously enforced multi-features joint and considered the complementation among decorrelated features． Extensive comparison experiments on data set show that the proposed method can obtained idea sparsity and is competitive to the stateof-the-art methods for image classification，which demonstrated the efficiency of L2，1 and L1 mixed-norm algorithm
for extracting key information of an image．

Key words: sparse representation, multi-task learning, decorrelated, image classification