Background: Low disease activity state has been defined using SLEDAI and used as treatment target in SLE. However, there not any such definition BILAG-2004 index (BILAG-2004). Objectives: This study was to determine if low according is valid for use We also assessed longitudinally systems tally (BST). BST an alternative way of representing scores that combines the flexibility simplification num...

We investigate the DFT + U approach as a viable solution to describe the low-lying states of ligated and unligated iron heme complexes. Besides their central role in organometallic chemistry, these compounds represent a paradigmatic case where LDA, GGA, and common hybrid functionals fail to reproduce the experimental magnetic splittings. In particular, the imidazole pentacoordinated heme is inc...

The matrix-based LDA method is attracting increasing attention. Compared with classic LDA, this method can overcome the small sample size (SSS) problem. However, previous literatures neglect the fact that there are two available matrix-based LDA algorithms and usually use only one of the two algorithms to perform the experiment. By experimental analysis, this work point out the combination of t...

In this paper, we propose a new classification method using composite features, each of which consists of a number of primitive features. The covariance of two composite features contains information on statistical dependency among multiple primitive features. A new discriminant analysis (C-LDA) using the covariance of composite features is a generalization of the linear discriminant analysis (...

Although face verification systems have proven to be reliable in ideal environments, they can be very sensitive to real environmental conditions. The system robustness can be increased by the fusion of different face verification algorithms. To the best of our knowledge, no face verification system tried exploiting the fusion of LDA and PCA. In our opinion, the apparent strong correlation of LD...

Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Basically, in LDA the image always needs to be transformed into 1D vector, however recently twodimensional PCA (2DPCA) technique have been proposed. In 2DPCA, PCA technique is applied directly on the original images wit...

Linear discriminant analysis (LDA) is one of the most popular dimension reduction methods, but it is originally focused on a single-labeled problem. In this paper, we derive the formulation for applying LDA for a multi-labeled problem. We also propose a generalized LDA algorithm which is effective in a high dimensional multi-labeled problem. Experimental results demonstrate that by considering ...

We propose a novel approach to boosting weighted linear discriminant analysis (LDA) as a weak classifier. Combining Adaboost with LDA allows to select the most relevant features for classification at each boosting iteration, thus benefiting from feature correlation. The advantages of this approach include the use of a smaller number of weak learners to achieve a low error rate, improved classif...

Linear Discriminant Analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications such as face recognition. Recently, a novel LDA algorithm based on QR Decomposition, namely LDA/QR, has been proposed, which is competitive in terms of classification accuracy with other LDA algorithms, but it has much lower costs in time and spa...