Sentence examples for data manifold from inspiring English sources

Specifically, we model the data manifold using a similarity graph with local and geometrical consistency properties.

Once acquiring the vanishing component, the natural feature of a data manifold pattern can be captured.

Geometrically, this is equivalent to thinking of a data manifold or subspace.

The significance of our research is fourfold: 1) The MPCJRF is an underlying adjustment, with respect to the pairwise constraints, to the graph Laplacian enlisted for approximating the potential data manifold.

This type of adjustment plays the correction role, as an unbiased estimation of the data manifold is difficult to obtain, whereas the pairwise constraints, converted from the given labels, have an overall high confidence level.

D. Cai at al. [31] study the locality sensitive discriminant analysis (LSDA) method, which utilizes local geometry structure of the data manifold and discriminant information at the same time.

In particular, we design a low complexity solution that is able to exploit the properties of the data manifolds with a graph-based algorithm.

Manifold regularization (MR) is a promising regularization framework for semi-supervised learning, which introduces an additional penalty term to regularize the smoothness of functions on data manifolds and has been shown very effective in exploiting the underlying geometric structure of data for classification.

In this paper, we propose a novel unsupervised non-parametric kernel learning method, which can seamlessly combine the spectral embedding of unlabeled data and manifold Regularized Least-Squares (RLS) to learn non-parametric kernels efficiently.

Environmental modelers are now faced with the challenge of determining how data from manifold sources, types of process-based and empirical models, and hardware/software computing infrastructure can be reliably integrated and applied to protect human health and the environment.

Liu et al. proposed a novel unsupervised non-parametric kernel learning method, which can seamlessly combine the spectral embedding of unlabeled data and manifold regularized least squares to learn non-parametric kernels efficiently [18].