Details

Autor(en) / Beteiligte

• Timilsina, Mohan
• Figueroa, Alejandro
• d’Aquin, Mathieu
• Yang, Haixuan

Titel

Semi-supervised regression using diffusion on graphs

Ist Teil von

• Applied soft computing, 2021-06, Vol.104, p.107188, Article 107188

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• In real-world machine learning applications, unlabeled training data are readily available, but labeled data are expensive and hard to obtain. Therefore, semi-supervised learning algorithms have gathered much attention. Previous studies in this area mainly focused on a semi-supervised classification problem, whereas semi-supervised regression has received less attention. In this paper, we proposed a novel semi-supervised regression algorithm using heat diffusion with a boundary-condition that guarantees a closed-form solution. Experiments from artificial and real datasets from business, biomedical, physical, and social domain show that the boundary-based heat diffusion method can effectively outperform the top state of the art methods. •A novel graph-based approach for predicting real labeled values of the nodes.•The proposed algorithm uses heat diffusion with a boundary-condition.•Experiments in various data show effectiveness of our approach.