A Metric-based Principal Curve Approach for Learning One-dimensional Manifold
<|reference_start|>A Metric-based Principal Curve Approach for Learning One-dimensional Manifold: 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.<|reference_end|>
@article{cui2024a,
    title={A Metric-based Principal Curve Approach for Learning One-dimensional
  Manifold},
    author={Elvis Han Cui, Sisi Shao},
    journal={arXiv preprint arXiv:2405.12390},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12390},
    primaryClass={stat.ML cs.AI cs.LG stat.AP}
}