A Metric-based Principal Curve Approach for Learning One-dimensional Manifold
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912281694044160 |
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| author | Cuicizion, Eliuvish |
| author_facet | Cuicizion, Eliuvish |
| contents | Principal curve is a well-known statistical method oriented in manifold learning using concepts from differential geometry. In this paper, we propose a novel metric-based principal curve (MPC) method that learns one-dimensional manifold of spatial data. Synthetic datasets Real applications using MNIST dataset show that our method can learn the one-dimensional manifold well in terms of the shape. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12390 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Metric-based Principal Curve Approach for Learning One-dimensional Manifold Cuicizion, Eliuvish Machine Learning Artificial Intelligence Applications Principal curve is a well-known statistical method oriented in manifold learning using concepts from differential geometry. In this paper, we propose a novel metric-based principal curve (MPC) method that learns one-dimensional manifold of spatial data. Synthetic datasets Real applications using MNIST dataset show that our method can learn the one-dimensional manifold well in terms of the shape. |
| title | A Metric-based Principal Curve Approach for Learning One-dimensional Manifold |
| topic | Machine Learning Artificial Intelligence Applications |
| url | https://arxiv.org/abs/2405.12390 |