Novel £2,1-Norm Based Least Square Regression Method for Classification

In the realm of linear regression, the concept of utilizing relaxed regression targets for classification has shown considerable success. However, it suffers from a lack of strong discriminative ability. In this study, we build upon the theory of relaxed regression targets and propose a more concise and discriminative model for multi-class classification. Our approach introduces the £
2,1
-norm regularized term to enhance the efficacy and compactness of the learned projection matrix. This regularization term not only generates a sparse row structure but also facilitates feature selection during the training phase. Consequently, both the accuracy of classification and the convergence speed of the model are improved. Furthermore, when compared to various variants of the linear regression model, our designed method exhibits superior performance in image classification. Through extensive experiments conducted on image databases, we provide evidence that our proposed approach excels in recognition capability, surpassing the performance of state-of-the-art methods in terms of classification accuracy.