A Quasi-isometric Embedding Algorithm
Machine Learning
2017-11-06 v3 Computational Geometry
Machine Learning
Abstract
The Whitney embedding theorem gives an upper bound on the smallest embedding dimension of a manifold. If a data set lies on a manifold, a random projection into this reduced dimension will retain the manifold structure. Here we present an algorithm to find a projection that distorts the data as little as possible.
Cite
@article{arxiv.1709.01972,
title = {A Quasi-isometric Embedding Algorithm},
author = {David W. Dreisigmeyer},
journal= {arXiv preprint arXiv:1709.01972},
year = {2017}
}