Manifold learning theory and applications / [edited by] Yunqian Ma, and Yun Fu. - xxiv, 290 p., [16] p. of plates : ill. (some col.) ; 27 cm.

Contents

Spectral embedding methods for manifold learning
introduction
spaces and manifolds
data on manifolds
e.tc

Robust Laplacian eigenmaps using global information
introduction
graph Laplacian
global information on manifold
e.tc

Density preserving maps
introduction
the existence of density preserving maps
density estimation on submanifolds
preserving the estimated density
e.tc

Sample complexity in manifold learning
introduction
sample complexity of classification on a manifold
learning smooth class boundaries
e.tc

Manifold alignment
introduction
formalization and analysis
variants of Manifold alignment
e.tc

Large-scale manifold learning
introduction
background
comparison of sampling methods
large scale manifold learning
e.tc

Metric and heat kernel
introduction
theoretic background
discrete heat kernel
heat kernel simplification
e.tc

Discreet Ricci flow for surface and 3-manifold
introduction
theoretic background
surface Ricci flow
e.tc


2D and 3D objects morphing using manifold techniques
introduction
interpolation on Euclidean spaces
generalization of interpolation algorithms on a Manifold M
e.tc

Learning image manifolds from local features
introduction
joint feature-spatial embedding
solving the out of sample problem
e.tc

Human motion analysis applications of manifold learning
introduction
learning a simple motion manifold
factorized generative models
e.tc


Includes bibliographical references p. 2275-280 and index p. 281-290

9781439871096 (hbk.)


Manifolds (Mathematics)

516.07 / MAN