Semi-supervised regression using diffusion on graphs
| dc.contributor.author | Timilsina, M. | |
| dc.contributor.author | Figueroa, A. | |
| dc.contributor.author | d'Aquin, M. | |
| dc.contributor.author | Yang, H. | |
| dc.date.accessioned | 2021-05-12T21:27:20Z | |
| dc.date.available | 2021-05-12T21:27:20Z | |
| dc.date.issued | 2021-06 | |
| dc.description | Indexación Scopus | es |
| dc.description.abstract | 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) | es |
| dc.description.uri | https://www-sciencedirect-com.recursosbiblioteca.unab.cl/science/article/pii/S1568494621001113?via%3Dihub | |
| dc.identifier.citation | Applied Soft Computing, Volume 104, June 2021, Article number 107188 | es |
| dc.identifier.doi | 10.1016/j.asoc.2021.107188 | |
| dc.identifier.issn | 15684946 | |
| dc.identifier.uri | http://repositorio.unab.cl/xmlui/handle/ria/18837 | |
| dc.language.iso | en | es |
| dc.publisher | Elsevier Ltd | es |
| dc.subject | Manifold Learning | es |
| dc.subject | Locally Linear Embedding | es |
| dc.subject | Dimensionality Reduction | es |
| dc.subject | Boundary heat diffusion | es |
| dc.title | Semi-supervised regression using diffusion on graphs | es |
| dc.type | Artículo | es |
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