Linear discriminant analysis (LDA) finds an orientation that projects high dimensional feature vectors to reduced dimensional feature space in such a way that the overlapping between the classes in this feature space is minimum. This overlapping is usually finite and produces finite classification error which is further minimized by rotational LDA technique. This rotational LDA technique rotate...

Neutron diffraction with isotope substitution is used to determine the structures of high (HDA) and low (LDA) density amorphous ice. Both "phases" are fully hydrogen bonded, tetrahedral networks, with local order similarities between LDA and ice Ih, and HDA and liquid water. Moving from HDA, through liquid water and LDA to ice Ih, the second shell radial order increases at the expense of spatia...

In this paper, a combination methodology of Discrete Cosine Transform (DCT) and an improved D-LDA and Neural Networks was proposed. DCT can compress the information of original signal efficiently, so we reduce the dimension firstly and then extract features by improved D-LDA on the low dimension space to overcome the shortages of LDA maximally. After calculating the eigenvectors and a new Fishe...

In this paper we introduce a fuzzy constraint linear discriminant analysis (FC-LDA). The FC-LDA tries to minimize misclassification error based on modified perceptron criterion that benefits handling the uncertainty near the decision boundary by means of a fuzzy linear programming approach with fuzzy resources. The method proposed has low computational complexity because of its linear character...

We introduce a generalization (gLDA) of the traditional Local Density Approximation (LDA) within density functional theory. The gLDA uses both the one-electron Seitz radius rs and a two-electron hole curvature parameter η at each point in space. The gLDA reduces to the LDA when applied to the infinite homogeneous electron gas but, unlike the LDA, is is also exact for finite uniform electron gas...

We introduce a generalization (gLDA) of the traditional Local Density Approximation (LDA) within density functional theory. The gLDA uses both the one-electron Seitz radius rs and a two-electron hole curvature parameter η at each point in space. The gLDA reduces to the LDA when applied to the infinite homogeneous electron gas but, unlike the LDA, it is also exact for finite uniform electron gas...

The Latent Dirichlet Location (LDA) model is a popular method for creating mixed-membership clusters. Despite having been originally developed text analysis, LDA has used wide range of other applications. We propose new formulation the which incorporates covariates. In this model, negative binomial regression embedded within LDA, enabling straight-forward interpretation coefficients and analysi...