A multi-view classification and feature selection method via sparse low-rank regression analysis

In recent years, multi-view classification and feature selection methods have received close attention in many fields. However, in many practical classification problems, the data in each view may contain a lot of noises. In addition, when data are of high dimensions and small sample attributes, it is difficult to remove redundant features in feature selection experiments. To deal with these problems well, the sparse multi-view low-rank regression method is proposed in this paper. The method based on sparse and low-rank theory introduces the penalty factors in the matrix transformation process to decompose the matrix into sparse and low-rank results. The model is constructed by imposing L₂-norm and L_2,1-norm constraints on the objective function. Experimental results on sequencing data show that the proposed method has superior performance over several state-of-the-art methods in multi-view classification and feature selection.