Proposing the Curvature-Augmented Manifold Embedding and Learning (CAMEL) method as a novel approach to dimensional reduction and data visualization.
This paper introduces a novel chart autoencoder architecture for semi-supervised manifold learning that leverages asymmetric encoding-decoding processes and incorporates label information to effectively represent complex topological structures and functions on manifolds.