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 ...

Dust particle contamination in plasma etching reactors continues to be a major concern to microelectronic device manufacturers. Dust particulates as small as tens of nanometers in dimension can produce killer defects on the substrate surface. These contaminants are produced either through gas-phase nucleation processes in the discharge, or through interaction with surfaces in the reactor. Their...

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...

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...

This study investigated the morphological and mineral features of nano zero-valent (nZVI)-induced iron precipitation on quartz particles. Lead was employed as artificial contaminant, while used to mimic inert solid particles in soil. The characterisation indicate that minerals precipitated heterogeneously surface with plush-like flake-like structures. They are made deuterogenic plumbiferous fer...

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...