Semi-supervised graph-based retargeted least squares regression

- A semi-supervised graph-based retargeted least squares regression model is proposed for multicategory classification.
- Our aim is to utilize a graph regularization to restrict the regression labels of ReLSR, such that similar samples should have similar regression labels.
- Linear squares regression and graph construction are unified into a same framework to achieve an overall optimum.

In this paper, we propose a semi-supervised graph-based retargeted least squares regression model (SSGReLSR) for multicategory classification. The main motivation behind SSGReLSR is to utilize a graph regularization to restrict the regression labels of ReLSR, such that similar samples should have similar regression labels. However, in SSGReLSR, constructing the graph structure and learning the regression matrix are two independent processes, which can't guarantee an overall optimum. To overcome this shortage of SSGReLSR, we also propose a semi-supervised graph learning retargeted least squares regression model (SSGLReLSR), where linear squares regression and graph construction are unified into a same framework to achieve an overall optimum. To optimize our proposed SSGLReLSR, an efficient iteration algorithm is proposed. Extensive experiments results confirm the effectiveness of our proposed methods.