60 search results - page 1 / 12 » Continuous nonlinear dimensionality reduction by kernel Eige... |
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
Non-linear dimensionality reduction of noisy data is a challenging problem encountered in a variety of data analysis applications. Recent results in the literature show that spect...
ICIP
2010
IEEE
13 years 2 months ago
2010
IEEE
Many classes of image data span a low dimensional nonlinear space embedded in the natural high dimensional image space. We adopt and generalize a recently proposed dimensionality ...
ICML
2005
IEEE
14 years 5 months ago
2005
IEEE
Many unsupervised algorithms for nonlinear dimensionality reduction, such as locally linear embedding (LLE) and Laplacian eigenmaps, are derived from the spectral decompositions o...