GULSTSVM: A fusion of graph information and universum learning in twin SVM

Abstract

In several applications, the datasets have an underlying graphical structure, and geometric information about the data is needed in the learning algorithm. Universum data serves as a useful resource for classification problems by providing prior information about the data distribution. However, the graph connectivity information embedded in the universum data has not been utilized in previous algorithms. To address this problem, a novel graph based algorithm is proposed in this work to infuse connectivity information of universum in the optimization problem of the classifier. The proposed algorithm is termed as graph based universum least squares twin support vector machine (GULSTSVM). The proposed algorithm involves manifold regularization on the universum graph to provide geometric information to the classifier. The solution of the proposed algorithm involves a system of linear equations, making it efficient in terms of training time. Moreover, to efficiently capture local and global connectivity information of universum data, a novel multi-hop connectivity method is also proposed in this work. The multi-hop approach provides a fusion of local and global graph connectivity. A concept of minimum spanning tree is presented to capture local connectivity, and feature aggregation is performed to obtain global connectivity information. Experimental results on synthetic and real-world benchmark datasets show the advantages and applicability of the proposed algorithm. © 2026 Elsevier B.V.