Sentence examples for manifold learning algorithm from inspiring English sources

A new kernel-based supervised manifold learning algorithm, called DKLLE, is proposed for facial expression recognition.

Then, manifold learning algorithm is utilized to decompose feature matrix to be a subspace, that is, manifold subspace.

The manifold learning algorithm seeks to keep local relationship of the feature matrix, which is more meaningful for damage assessment.

In this paper, a new supervised manifold learning algorithm (S-LapEig) for feature extraction is proposed first.

Therefore, the manifold learning algorithm locality preserving projection (LPP) was introduced to extract the characteristic features and to reduce the dimension.

A manifold learning algorithm, called nearest feature line embedding (NFLE) [18], reduces the dimensionality of color features for reducing the illumination impacts.

Recently, the manifold learning algorithm-local preserving projection (LPP) is proposed [7].

Besides, manifold learning algorithms not only reduce the feature dimensions but also preserve the sample relationship of the same classes under various illumination conditions.

LPP [17] and LLE [16] are two popular manifold learning algorithms which are applied to keep the manifold structure of samples.

Manifold learning algorithms can be divided into global linear dimension reduction approaches, including Principle Component Analysis (PCA) and Multiple Dimension Scaling (MDS), global nonlinear approaches, for instance, ISOMAP [ 24], local linear approaches, including Locally Linear Embedding (LLE) [ 25] and the Laplacian Eigenmap [ 26].

Inspired by the advantages of the matrix learning and the SSL, in this paper, we propose a novel semi-supervised matrix learning algorithm by incorporating the manifold regularization into the matrixized least squares support vector machine (MatLSSVM), termed as Laplacian matrixized LSSVM, or LapMatLSSVM for short.