Timilsina, M.Figueroa, A.d'Aquin, M.Yang, H.2021-05-122021-05-122021-06Applied Soft Computing, Volume 104, June 2021, Article number 10718815684946http://repositorio.unab.cl/xmlui/handle/ria/18837Indexación ScopusIn 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. © 2021 The Author(s)enManifold LearningLocally Linear EmbeddingDimensionality ReductionBoundary heat diffusionArtículo10.1016/j.asoc.2021.107188