A new locally linear embedding scheme in light of Hessian eigenmap

12/16/2021
by   Liren Lin, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/06/2017

A Quasi-isometric Embedding Algorithm

The Whitney embedding theorem gives an upper bound on the smallest embed...
research
08/25/2019

Locally Linear Image Structural Embedding for Image Structure Manifold Learning

Most of existing manifold learning methods rely on Mean Squared Error (M...
research
08/28/2021

Avoiding unwanted results in locally linear embedding: A new understanding of regularization

We demonstrate that locally linear embedding (LLE) inherently admits som...
research
03/22/2023

Convergence of Hessian estimator from random samples on a manifold

We provide a systematic convergence analysis of the Hessian operator est...
research
06/16/2008

Manifold Learning: The Price of Normalization

We analyze the performance of a class of manifold-learning algorithms th...
research
04/09/2018

Connecting Dots -- from Local Covariance to Empirical Intrinsic Geometry and Locally Linear Embedding

Local covariance structure under the manifold setup has been widely appl...

Please sign up or login with your details

Forgot password? Click here to reset