We illustrate the GMST approach on standard synthetic manifolds as well as on real data sets consisting of images of faces. Index Terms—Nonlinear dimensionality ...

In this paper, we consider the closely related problem of estimating the manifold's intrinsic dimension and the intrinsic entropy of the sample points.

We illustrate the GMST approach on standard synthetic manifolds as well as on real data sets consisting of images of faces. Index Terms—Conformal embedding, ...

This paper focuses on the geodesic-minimal-spanning-tree (GMST) method, which uses the overall lengths of the MSTs to simultaneously estimate manifold ...

The GMST method simply constructs a minimal spanning tree (MST) sequence using a geodesic edge matrix and uses the overall lengths of the MSTs to simultaneously ...

The goal of this paper is to develop an algorithm that jointly estimates both the intrinsic dimension and intrinsic entropy on the manifold, without knowing the ...

Geodesic entropic graphs for dimension and entropy estimation in manifold learning. Author: COSTA, Jose A1 ; HERO, Alfred O1

• Can use entropic graph methods to obtain consistent estimators of dimension and entropy of samples on a manifold. • Manifold learning and model reduction.

Aug 1, 2004 · In this paper, we consider the closely related problem of estimating the manifold's intrinsic dimension and the intrinsic entropy of the sample ...

Entropic graphs can be used to construct consistent estimators of entropy and information divergence. Robustification to outliers via pruning.