  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.
