148 search results - page 7 / 30 » Riemannian Manifold Learning for Nonlinear Dimensionality Re... |
CVPR
2008
IEEE
15 years 11 months ago
2008
IEEE
We consider the problem of dimensionality reduction, where given high-dimensional data we want to estimate two mappings: from high to low dimension (dimensionality reduction) and f...
ICONIP
2004
14 years 11 months ago
2004
Algorithms for nonlinear dimensionality reduction (NLDR) find meaningful hidden low-dimensional structures in a high-dimensional space. Current algorithms for NLDR are Isomaps, Loc...
73
Voted
IJCAI
2007
14 years 11 months ago
2007
Dimensionality reduction is a much-studied task in machine learning in which high-dimensional data is mapped, possibly via a non-linear transformation, onto a low-dimensional mani...
79
Voted
ICPR
2010
IEEE
15 years 1 days ago
2010
IEEE
—A novel non-linear dimensionality reduction method, called Temporal Laplacian Eigenmaps, is introduced to process efficiently time series data. In this embedded-based approach,...
ICML
2004
IEEE
15 years 10 months ago
2004
IEEE
