Low-dose aspirin (LDA) is thought to prevent preeclampsia in high-risk pregnancy, but it is not universally used out of concern for its efficacy and safety. The authors meta-analyzed 29 randomized controlled trials (RCTs) to evaluate LDA for preventing preeclampsia and its complications. LDA can reduce the incidence of preeclampsia (odds ratio [OR], 0.71; 95% confidence interval [CI], 0.57-0.87...

Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques are important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in the traditional PCA and LDA some weaknesses. In this paper, we propose a new Line-based methodes called Line-based PCA and Line-based LDA that ...

Linear discriminant analysis (LDA) is a widely-used feature extraction method in classification. However, the original LDA has limitations due to the assumption of a unimodal structure for each cluster, which is not satisfied in many applications such as facial image data when variations, e.g. angle and illumination, can significantly influence the images. In this paper, we propose a novel meth...

6Li and 15N NMR spectroscopic studies show that hexane solutions of LDA containing <1.0 equiv of N,N,N′,N′′,N′′-pentamethyldiethylenetriamine (PMDTA) per lithium contain a mixture of unsolvated LDA oligomers, monosolvated open dimer, and monosolvated monomer. At >1.0 equiv of PMDTA per lithium, monomer is the dominant species. Addition of PMDTA to LDA in toluene affords open dimer at low [PMDTA...

Fisher linear discriminant analysis (LDA) can be sensitive to the problem data. Robust Fisher LDA can systematically alleviate the sensitivity problem by explicitly incorporating a model of data uncertainty in a classification problem and optimizing for the worst-case scenario under this model. The main contribution of this paper is show that with general convex uncertainty models on the proble...

This paper presents Generalized Correspondence-LDA (GC-LDA), a generalization of the Correspondence-LDA model that allows for variable spatial representations to be associated with topics, and increased flexibility in terms of the strength of the correspondence between data types induced by the model. We present three variants of GC-LDA, each of which associates topics with a different spatial ...

Linear discriminant analysis (LDA) is a popular dimensionality reduction and classification method that simultaneously maximizes between-class scatter and minimizes within-class scatter. In this paper, we verify the equivalence of LDA and least squares (LS) with a set of dependent variable matrices. The equivalence is in the sense that the LDA solution matrix and the LS solution matrix have the...

Linear Discriminant Analysis (LDA) followed by a diagonalizing maximum likelihood linear transform (MLLT) applied to spliced static MFCC features yields important performance gains as compared to MFCC+dynamic features in most speech recognition tasks. It is reasonable to regularize LDA transform computation for stability. In this paper, we regularize LDA and heteroschedastic LDA transforms usin...

Structural and mechanistic studies of the lithium diisopropylamide (LDA)-mediated anionic Fries rearrangements of aryl carbamates are described. Substituents at the meta position of the arene (H, OMe, F) and the dialkylamino moiety of the carbamate (Me(2)N, Et(2)N, and i-Pr(2)N) markedly influence the relative rates of ortholithiation and subsequent Fries rearrangement. Structural studies using...

In this paper, a novel face recognition method based on Gabor-wavelet and linear discriminant analysis (LDA) is proposed. Given training face images, discriminant vectors are computed using LDA. The function of the discriminant vectors is two-fold. First, discriminant vectors are used as a transform matrix, and LDA features are extracted by projecting original intensity images onto discriminant...