Multiple classifier systems provide an effective way to improve pattern recognition performance. In this paper, we use multiple classifier combination to improve LDA for high dimensional data classification. When dealing with the high dimensional data, LDA often suffers from the small sample size problem and the constructed classifier is biased and unstable. Although some approaches, such as PC...

It has been demonstrated that the Linear Discriminant Analysis (LDA) approach outperforms the Principal Component Analysis (PCA) approach in face recognition tasks. Due to the high dimensionality of a image space, many LDA based approaches, however, first use the PCA to project an image into a lower dimensional space or so-called face space, and then perform the LDA to maximize the discriminato...

A new LDA-based face recognition system is presented in this paper. Linear discriminant analysis (LDA) is one of the most popular linear projection techniques for feature extraction. The major drawback of applying LDA is that it may encounter the small sample size problem. In this paper, we propose a new LDA-based technique which can solve the small sample size problem. We also prove that the m...

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 ...