Semi-supervised regression using diffusion on graphs

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. © 2021 The Author(s)

Notas

Indexación Scopus

Palabras clave

Manifold Learning, Locally Linear Embedding, Dimensionality Reduction, Boundary heat diffusion

Citación

Applied Soft Computing, Volume 104, June 2021, Article number 107188

DOI

10.1016/j.asoc.2021.107188

URI

http://repositorio.unab.cl/xmlui/handle/ria/18837

Colecciones

FIng - Artículos de Revista

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