… , many traditional embedding algorithms only deal with one … propose a novel Discriminant
Laplacian Embedding (DLE) … discriminativity of the resulted embedding space. By solving the …

… This work proposes to place the emotion detection problem under the framework of Discriminant
Laplacian Embedding (DLE) to integrate these two types of facial expression data in a …

… We will name the linear discriminant embedding obtained by optimising J1(w) (equation 3)
as LDE-I, and the embedding obtained from J2(w) (equation 9) as LDE-II. For clarity of …

We present a new approach, called local discriminant embedding (LDE), to manifold learning
and pattern classification. In our framework, the neighbor and class relations of data are …

… discriminant vectors is equal to the sum of the corresponding eigenvalues. Because the
eigenvalues reflect the discrimination ability, we use only the discriminant … d discriminant vectors …

… has incorporated K-means and linear discriminant analysis to promote clustering and tackle
… Practically the K eigenvectors can be considered as the K-dimensional embedding of the n …

… The nonlinear methods such as locally linear embedding (LLE) [6] and Laplacian … The
classical linear embedding methods (eg, principal component analysis (PCA), linear discriminant …

… problem by embedding the data on low dimensional manifolds which preserve multi-label
correlation. For this, we apply the multi-label Discriminant Laplacian Embedding (DLE) …

… representation can still discover the latent discriminant information and thus provides better
measure coefficients and significant discriminant abilities for feature extraction. Moreover, …

… Neighborhood preserving embedding (NPE) has been widely used to learn the intrinsic …
reduction approach, namely discriminant neighborhood structure embedding (DNSE). DNSE …