140 search results - page 3 / 28 » Dimensionality Reduction with Adaptive Kernels |
ICCV
2009
IEEE
14 years 9 months ago
2009
IEEE
We present a manifold learning approach to dimensionality
reduction that explicitly models the manifold as a mapping
from low to high dimensional space. The manifold is
represen...
ICML
2004
IEEE
14 years 5 months ago
2004
IEEE
IJCAI
2003
13 years 6 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...
AUSAI
2006
Springer
13 years 8 months ago
2006
Springer
In this paper, we propose the Kernel Laplacian Eigenmaps for nonlinear dimensionality reduction. This method can be extended to any structured input beyond the usual vectorial data...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
In this paper we investigate the regularization property of Kernel Principal Component Analysis (KPCA), by studying its application as a preprocessing step to supervised learning ...