Discriminative Graph Learning via Low-Rank Constraint for Semi-Supervised Learning

Graph learning is more and more widely used in discovering associations between data and mining relationships between data. Our paper proposed a discriminative graph learning for semi-supervised learning method discriminative graph learning with low rank constraints. Traditional semi-supervised learning methods exist the problem of similarity between similar species is not high. How to improve the similarity between similar species has become a problem we need to solve in this field. In order to solve this problem, this paper proposed a new semi-supervised learning means, which mainly includes two parts: 1) Representing the local structure of the research sample by low-rank representation, compressing similar content, and easily finding the focus of image classification; 2) in order to improve the similarity between classes, A term that uses similarity constraints between the data to be classified and a known model. In our paper, the proposed means is used for comparative experiments of classification on multiple data sets. Through experiments show that the means proposed in this paper performs better than traditional semi-supervised classification methods in terms of accuracy of classification.