148 search results - page 4 / 30 » Riemannian Manifold Learning for Nonlinear Dimensionality Re... |
SDM
2007
SIAM
13 years 6 months ago
2007
SIAM
Manifold learning is an effective methodology for extracting nonlinear structures from high-dimensional data with many applications in image analysis, computer vision, text data a...
JMLR
2010
13 years 3 months ago
2010
Nonlinear dimensionality reduction methods are often used to visualize high-dimensional data, although the existing methods have been designed for other related tasks such as mani...
ADCM
2008
13 years 4 months ago
2008
Learning function relations or understanding structures of data lying in manifolds embedded in huge dimensional Euclidean spaces is an important topic in learning theory. In this ...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
We study the problem of discovering a manifold that best preserves information relevant to a nonlinear regression. Solving this problem involves extending and uniting two threads ...
PR
2006
13 years 4 months ago
2006
In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear manifold learning methods have aroused a great deal of interest in the machine learning communit...