Standard LDA model suffers the problem that the topic assignment of each word is independent and word correlation hence is neglected. To address this problem, in this paper, we propose a model called Word Related Latent Dirichlet Allocation (WR-LDA) by incorporating word correlation into LDA topic models. This leads to new capabilities that standard LDA model does not have such as estimating in...

Linear Discriminant Analysis (LDA) is a popular feature extraction technique for face image recognition and retrieval. However, It often suffers from the small sample size problem when dealing with the high dimensional face data. Two-step LDA (PCA+LDA) [1–3] is a class of conventional approaches to address this problem. But in many cases, these LDA classifiers are overfitted to the training set...

In face recognition, LDA often encounters the so-called small sample size (SSS) problem, also known as curse of dimensionality. This problem occurs when the dimensionality of the data is quite large in comparison to the number of available training images. One of the approaches for handling this situation is the subspace LDA. It is a two-stage framework: it first uses PCA-based method for dimen...

Recently a kind of matrix-based discriminant feature extraction approach called 2DLDA have been drawn much attention by researchers. 2DLDA can avoid the singularity problem and has low computational costs and has been experimentally reported that 2DLDA outperforms traditional LDA. In this paper, we compare 2DLDA with LDA in view of the discriminant power and find that 2DLDA as a kind of special...

OBJECTIVES The objectives of this study are to examine the efficacy and safety of low-dose aspirin (LDA) vs LDA plus low-intensity warfarin (LDA + W) in the primary thrombosis prevention of aPL-positive patients with SLE and/or obstetric morbidity and the role of clinical and serological markers in the development of thrombosis. METHODS In this 5-year prospective, randomized, open, controlled...

The density functional based calculations of the electronic structure of YMnO3 are performed for selected spin configurations and from the differences of the total energies the exchange integrals are determined. To improve the description of strongly correlated 3d electrons of Mn the LDA + U method is employed. The strongest exchange interaction is found between the nearest neighbor Mn spins, t...

We present results of GGA+U calculations for the “d magnet” RbO2, where magnetic properties are due to partially filled oxygen p orbitals. We show that on-site interactions on the oxygen sites lead to a strong tendency towards the formation of an orbitally polarized insulating state, in contrast to the half-metallic behavior predicted for this class of compounds within pure LDA/GGA. The obtaine...

We use first-principles calculations to study the effect of magnetic disorder and electron correlations on the structural and thermodynamic properties of CrN. We illustrate the usability of a special quasirandom structure supercell treatment of the magnetic disorder by comparing with coherent potential approximation calculations and with a complementary magnetic sampling method. The need of a t...

The identification and discrimination of 2 closely related and morphologically similar species of Gyrodactylus, G. salaris and G. thymalli, were assessed using the statistical classification methodologies Linear Discriminant Analysis (LDA) and k-Nearest Neighbours (KNN). These statistical methods were applied to morphometric measurements made on the gyrodactylid attachment hooks. The mean estim...

Source-normalised Linear Discriminant Analysis (SNLDA) was recently introduced to improve speaker recognition using i-vectors extracted from multiple speech sources. SNLDA normalises for the effect of speech source in the calculation of the between-speaker covariance matrix. Sourcenormalised-and-weighted (SNAW) LDA computes a weighted average of source-normalised covariance matrices to better e...