207 search results - page 8 / 42 » Regularized Principal Manifolds |
TNN
2010
13 years 2 months ago
2010
We consider the problem of semi-supervised feature selection, where we are given a small amount of labeled examples and a large amount of unlabeled examples. Since a small number ...
JMLR
2006
13 years 7 months ago
2006
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
ECCV
2000
Springer
14 years 9 months ago
2000
Springer
In recent years several techniques have been proposed for modelling the low-dimensional manifolds, or `subspaces', of natural images. Examples include principal component anal...
ICML
2007
IEEE
14 years 8 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 ...
ESANN
2007
13 years 9 months ago
2007
Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is specifically designed to...