166 search results - page 10 / 34 » Learning a kernel matrix for nonlinear dimensionality reduct... |
CORR
2008
Springer
13 years 8 months ago
2008
Springer
Maximum Variance Unfolding (MVU) and its variants have been very successful in embedding data-manifolds in lower dimensionality spaces, often revealing the true intrinsic dimensio...
PR
2007
13 years 8 months ago
2007
Isomap is one of widely-used low-dimensional embedding methods, where geodesic distances on a weighted graph are incorporated with the classical scaling (metric multidimensional s...
CVPR
2008
IEEE
14 years 10 months ago
2008
IEEE
We consider the task of dimensionality reduction for regression (DRR) whose goal is to find a low dimensional representation of input covariates, while preserving the statistical ...
ICCV
2007
IEEE
14 years 2 months ago
2007
IEEE
Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPC...
CVPR
2005
IEEE
14 years 10 months ago
2005
IEEE
We simultaneously approach two tasks of nonlinear discriminant analysis and kernel selection problem by proposing a unified criterion, Fisher+Kernel Criterion. In addition, an eff...