Graph-Based Semi-Supervised Segregated Lipschitz Learning
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909378038202368 |
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| author | Bozorgnia, Farid Belkheiri, Yassine Elmoataz, Abderrahim |
| author_facet | Bozorgnia, Farid Belkheiri, Yassine Elmoataz, Abderrahim |
| contents | This paper presents an approach to semi-supervised learning for the classification of data using the Lipschitz Learning on graphs. We develop a graph-based semi-supervised learning framework that leverages the properties of the infinity Laplacian to propagate labels in a dataset where only a few samples are labeled. By extending the theory of spatial segregation from the Laplace operator to the infinity Laplace operator, both in continuum and discrete settings, our approach provides a robust method for dealing with class imbalance, a common challenge in machine learning. Experimental validation on several benchmark datasets demonstrates that our method not only improves classification accuracy compared to existing methods but also ensures efficient label propagation in scenarios with limited labeled data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_03273 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph-Based Semi-Supervised Segregated Lipschitz Learning Bozorgnia, Farid Belkheiri, Yassine Elmoataz, Abderrahim Machine Learning Discrete Mathematics This paper presents an approach to semi-supervised learning for the classification of data using the Lipschitz Learning on graphs. We develop a graph-based semi-supervised learning framework that leverages the properties of the infinity Laplacian to propagate labels in a dataset where only a few samples are labeled. By extending the theory of spatial segregation from the Laplace operator to the infinity Laplace operator, both in continuum and discrete settings, our approach provides a robust method for dealing with class imbalance, a common challenge in machine learning. Experimental validation on several benchmark datasets demonstrates that our method not only improves classification accuracy compared to existing methods but also ensures efficient label propagation in scenarios with limited labeled data. |
| title | Graph-Based Semi-Supervised Segregated Lipschitz Learning |
| topic | Machine Learning Discrete Mathematics |
| url | https://arxiv.org/abs/2411.03273 |