Shamap: Shape-based Manifold Learning
Machine Learning
2019-09-20 v2 Machine Learning
Abstract
For manifold learning, it is assumed that high-dimensional sample/data points are embedded on a low-dimensional manifold. Usually, distances among samples are computed to capture an underlying data structure. Here we propose a metric according to angular changes along a geodesic line, thereby reflecting the underlying shape-oriented information or a topological similarity between high- and low-dimensional representations of a data cloud. Our results demonstrate the feasibility and merits of the proposed dimensionality reduction scheme.
Cite
@article{arxiv.1802.05386,
title = {Shamap: Shape-based Manifold Learning},
author = {Fenglei Fan and Ziyu Su and Yueyang Teng and Ge Wang},
journal= {arXiv preprint arXiv:1802.05386},
year = {2019}
}