TY - JOUR
T1 - Semi-supervised regression using diffusion on graphs
AU - Timilsina, Mohan
AU - Figueroa, Alejandro
AU - d'Aquin, Mathieu
AU - Yang, Haixuan
N1 - Funding Information:
We would like to acknowledge Science Foundation Ireland ( SFI/12/RC/2289_P2 ) for funding this research.
Publisher Copyright:
© 2021 The Author(s)
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - Boundary heat diffusion
KW - Label
KW - Network
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=85101385263&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2021.107188
DO - 10.1016/j.asoc.2021.107188
M3 - Article
AN - SCOPUS:85101385263
SN - 1568-4946
VL - 104
JO - Applied Soft Computing
JF - Applied Soft Computing
M1 - 107188
ER -