English

A new locally linear embedding scheme in light of Hessian eigenmap

Machine Learning 2021-12-17 v1 Machine Learning Numerical Analysis Numerical Analysis

Abstract

We provide a new interpretation of Hessian locally linear embedding (HLLE), revealing that it is essentially a variant way to implement the same idea of locally linear embedding (LLE). Based on the new interpretation, a substantial simplification can be made, in which the idea of "Hessian" is replaced by rather arbitrary weights. Moreover, we show by numerical examples that HLLE may produce projection-like results when the dimension of the target space is larger than that of the data manifold, and hence one further modification concerning the manifold dimension is suggested. Combining all the observations, we finally achieve a new LLE-type method, which is called tangential LLE (TLLE). It is simpler and more robust than HLLE.

Cite

@article{arxiv.2112.09086,
  title  = {A new locally linear embedding scheme in light of Hessian eigenmap},
  author = {Liren Lin and Chih-Wei Chen},
  journal= {arXiv preprint arXiv:2112.09086},
  year   = {2021}
}

Comments

13 pages

R2 v1 2026-06-24T08:20:53.057Z