Add to ORKGWe 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 of research. On the one hand, the literature on sufficient dimension reduction has focused on methods for finding the best linear subspace for nonlinear regression; we extend this to manifolds. On the other hand, the literature on manifold learning has focused on unsupervised dimensionality reduction; we extend this to the supervised setting. Our approach to solving the problem involves combining the machinery of kernel dimension reduction with Laplacian eigenmaps. Specifically, we optimize cross-covariance operators in kernel feature spaces that are induced by the...
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension redu...
Abstract: We review the ideas, algorithms, and numerical performance of manifold-based machine learn...
In recent years, manifold learning has become increasingly popular as a tool for performing non-line...
Manifold learning has gained in recent years a great attention in facing the problem of dimensionali...
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dim...
A high-dimensional regression space usually causes problems in nonlinear system identification.Howeve...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold ...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold ...
The trend today is to use many inexpensive sensors instead of a few expensive ones, since the same a...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold...
We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel metho...
We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel meth...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and man-ifold...
ber of non-linear dimensionality reduction techniques (manifold learners) have been proposed. Many o...
This article proposes a novel approach to linear dimension reduction for regression using nonparamet...
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension redu...
Abstract: We review the ideas, algorithms, and numerical performance of manifold-based machine learn...
In recent years, manifold learning has become increasingly popular as a tool for performing non-line...
Manifold learning has gained in recent years a great attention in facing the problem of dimensionali...
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dim...
A high-dimensional regression space usually causes problems in nonlinear system identification.Howeve...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold ...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold ...
The trend today is to use many inexpensive sensors instead of a few expensive ones, since the same a...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold...
We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel metho...
We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel meth...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and man-ifold...
ber of non-linear dimensionality reduction techniques (manifold learners) have been proposed. Many o...
This article proposes a novel approach to linear dimension reduction for regression using nonparamet...
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension redu...
Abstract: We review the ideas, algorithms, and numerical performance of manifold-based machine learn...
In recent years, manifold learning has become increasingly popular as a tool for performing non-line...