148 search results - page 14 / 30 » Riemannian Manifold Learning for Nonlinear Dimensionality Re... |
104
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IJCAI
2003
14 years 11 months ago
2003
We equate nonlinear dimensionality reduction (NLDR) to graph embedding with side information about the vertices, and derive a solution to either problem in the form of a kernel-ba...
112
Voted
MICCAI
2007
Springer
15 years 10 months ago
2007
Springer
Abstract. We introduce a non-linear shape prior for the deformable model framework that we learn from a set of shape samples using recent manifold learning techniques. We model a c...
ICML
2007
IEEE
15 years 10 months ago
2007
IEEE
This paper proposes a new map building framework for mobile robot named Localization-Free Mapping by Dimensionality Reduction (LFMDR). In this framework, the robot map building is...
103
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CVPR
2007
IEEE
15 years 11 months ago
2007
IEEE
We present an algorithm, Hierarchical ISOmetric SelfOrganizing Map (H-ISOSOM), for a concise, organized manifold representation of complex, non-linear, large scale, high-dimension...
68
Voted
ICML
2008
IEEE
15 years 10 months ago
2008
IEEE
In this paper we introduce a novel approach to manifold alignment, based on Procrustes analysis. Our approach differs from "semisupervised alignment" in that it results ...