A Metric-based Principal Curve Approach for Learning One-dimensional Manifold
EH Cui, S Shao - arXiv preprint arXiv:2405.12390, 2024 - arxiv.org
EH Cui, S Shao
arXiv preprint arXiv:2405.12390, 2024•arxiv.orgPrincipal 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.
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.
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.
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