Class-aware tensor factorization for multi-relational classification. Issue 2 (March 2020)

• Record Type: Journal Article
• Authors: Katsimpras, Georgios; Paliouras, Georgios
• Is Part Of: Information processing & management. Volume 57:Issue 2(2020:Mar.)
• Publication Date: 2020-03
• Subjects: Semi-supervised tensor factorization -- Multi-relational networks -- Social network analysis; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2019.102068
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: Introduced a new semi-supervised tensor factorization approach. Presented a joint optimization method which combines tensor factorization and a classification error term. Demonstrated effective performance in multiple real-world datasets. Abstract: In this paper, we propose a tensor factorization method, called CLASS-RESCAL, which associates the class labels of data samples with their latent representations. Specifically, we extend RESCAL to produce a semi-supervised factorization method that combines a classification error term with the standard factor optimization process. CLASS-RESCAL assimilates information from all the relations of the tensor, while also taking into account classification performance. This procedure forces the data samples within the same class to have similar latent representations. Experimental results on several real-world social network data indicate this is a promising approach for multi-relational classification tasks.