14 search results - page 1 / 3 » Dimensionality reduction using covariance operator inverse r... |
CVPR
2008
IEEE
14 years 9 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 ...
JMLR
2010
13 years 1 months ago
2010
This paper considers the problem of estimating a high dimensional inverse covariance matrix that can be well approximated by "sparse" matrices. Taking advantage of the c...
IDEAL
2010
Springer
13 years 5 months ago
2010
Springer
Dimension reduction for regression (DRR) deals with the problem of finding for high-dimensional data such low-dimensional representations, which preserve the ability to predict a ...
NIPS
2003
13 years 8 months ago
2003
We propose a novel method of dimensionality reduction for supervised learning. Given a regression or classification problem in which we wish to predict a variable Y from an expla...
ICML
2007
IEEE
14 years 7 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 ...