Jun 3, 2010 · A class of DRR methods exploits the notion of inverse regression (IR) to discover central subspaces. Whereas most existing IR techniques rely on ...
May 25, 2010 · In this paper, we propose the Covariance Operator Inverse. Regression (COIR), a novel nonlinear method for DRR that jointly exploits the ...
Whereas most existing IR techniques rely on explicit output space slicing, we propose a novel method called the Covariance Operator Inverse Regression (COIR) ...
COIR's unique properties make DRR applicable to problem domains with high-dimensional output data corrupted by potentially significant amounts of noise. Unlike ...
We consider the task of dimensionality reduction for re- gression (DRR) whose goal is to find a low dimensional rep- resentation of input covariates, ...
We consider the task of dimensionality reduc- tion for regression (DRR) informed by real- valued multivariate labels. The problem is.
Abstract: Central subspaces have long been a key concept for sufficient dimension reduction. Initially constructed for solving problems in the p<n.
To turn this formulation into an optimization problem, we characterize the notion of conditional independence using covariance operators on reproducing kernel ...
Missing: Central | Show results with:Central
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The goal is to reduce the dimension of the predictor vector without loss of information on the regression. We call this sufficient dimension reduction, ...