Relaxed group low rank regression model for multi-class classification

Least squares regression is an effective multi-classification method; however, in practical applications, many models based on the least squares regression method are significantly affected by noise (and outliers). Therefore, effectively reducing the adverse effects of noise is conducive to obtaining a better classification performance. Besides, preserving the intrinsic characteristics of samples to the greatest extent possible is beneficial for improving the discriminative ability of the model. Based on this analysis, we propose the relaxed group low-rank regression model for multi-class classification. The model effectively captures the hidden structural information of samples by exploiting the group low-rank constraint. Meanwhile, with the group low-rank constraint and the graph embedding constraint, the proposed method has more tolerance to noise (and outliers). The feature matrix with the L 21 -norm and the graph embedding constraint complement each other to capture the intrinsic characteristics of the samples. In addition, a sparsity error term with the L 21 norm is utilized to relax the strict target label matrix. These factors guarantee that the original samples are converted into a more compact and discriminative characteristic space. Finally, we compare the proposed model with various popular algorithms on several benchmark datasets. The experimental results demonstrate that the performance of the proposed method outperforms those of state-of-the-art methods.