Sentence examples for manifold learning and from inspiring English sources

We incorporate the manifold learning and the regression with a learned discriminant manifold-based projection function obtained by discriminatively Laplacian regularized least squares.

We juxtapose hypothesis testing, manifold learning, and harmonic analysis to obtain Multiscale Generalized Correlation (MGC).

By using techniques from manifold learning and optimal experimental design, our proposed approach can select the most informative features which can improve the learning performance the most.

The diffusion maps framework is a kernel-based method for manifold learning and data analysis that models a Markovian process over data.

This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology.

In this paper, we revisit the indentation imprint shape, using a novel approach involving image comparison by manifold learning, and show that it is able to outperform the conventional approach for a variety of cases.

Starting from classical methods such as Regularization Networks and Support Vector Machines, the course covers state of the art techniques based on the concepts of geometry (aka manifold learning), sparsity and a variety of algorithms for supervised learning (batch and online), feature selection, structured prediction and multitask learning.

Besides classic approaches such as Support Vector Machines, the course covers state of the art techniques exploiting data geometry (aka manifold learning), sparsity and a variety of algorithms for supervised learning (batch and online), feature selection, structured prediction and multitask learning.

Subsequently, the method classifies the candidate transients into spikes and non-spikes by learning the patterns of spikes using manifold learning, dimensionality reduction, and non-linear supervised classification.

Yubei Chen, Dylan Paiton, and Bruno Olshausen describe how sparse coding and manifold learning are connected, leading to a new unsupervised learning algorithm for simultaneously capturing sparse features and low-dimensional transformations of data.

Lead Guest Editor of the special issue on "Dimensionality Reduction and Manifold Learning" for IEEE Signal Processing Magazine, 2010.