Abstract: Classifiers based on Least Squares Regression(LSR)are effective in multi-classification tasks. However, most of the existing methods use limited projections and cause a lot of loss of discriminative information. Some algorithms only focus on the accurate fitting of the sample and the target matrix and ignore the problem of overfitting. In order to solve these problems and improve the classification performance, this paper proposes a multi-class image classification method based on low-rank inter-class sparsity discriminative least squares regression(LRICSDLSR). Introducing the inter-class sparsity constraint in the discriminative least squares regression model, so that the margin of samples from the same class can be greatly reduced, while the margin of samples from different classes can be increased; Apply low-rank constraints to the relaxed labels obtained from the non-negative relaxation matrix to improve their intra-class compactness and similarity; An additional regularization term is introduced on the learning label to avoid overfitting. Experimental results show that it helps to learn more distinguished regression projections to achieve better classification performance.The experimental results on a series of image data sets prove the effectiveness of the method.

Key words: low-rank, inter-class sparsity, image classification, regression, projection