This paper introduced the ideas of pairwise constraints and correlation analysis and proposed an orthogonal sub- space based nonlinear correlation learning for ...

OSNCL can project the multivariate data into a set of more useful features and preserve the intrinsic structure of the data and the pairwise constraints defined ...

In this paper, a new linear dimension reduction method called supervised orthogonal discriminant subspace projection (SODSP) is proposed, which addresses ...

Orthogonal subspace based nonlinear correlation learning for supervised dimensionality reduction. GrC 2009: 779-784. [c2]. view. electronic edition via DOI ...

In this paper we present a novel semiparametric method for dimensionality reduction that we refer to as Kernel Dimensionality Reduction (KDR). KDR is based on a ...

In this paper, a new linear dimension reduction method called supervised orthogonal discriminant subspace projection (SODSP) is proposed, which addresses ...

We propose a novel method of dimensionality reduction for supervised learning. Given a regression or classification problem in which we wish.

In this paper we present a novel semiparametric method for dimensionality reduction that we refer to as Kernel Dimensionality Reduction (KDR). KDR is based on ...

May 25, 2003 · Abstract. We propose a novel method of dimensionality reduction for supervised learning problems. Given a regression or classification ...

Nonlinear dimensionality reduction, also known as manifold learning, is any of various related techniques that aim to project high-dimensional data