Linear Discriminant Analysis (LDA) is a very common technique for dimensionality reduction problems as a preprocessing step for machine learning and pattern classification applications. At the same time, it is usually used as a black box, but (sometimes) not well understood. The aim of this paper is to build a solid intuition for what is LDA, and how LDA works, thus enabling readers of all leve...

Linear discriminant analysis (LDA) is a popular method in pattern recognition and is equivalent to Bayesian method when the sample distributions of different classes are obey to the Gaussian with the same covariance matrix. However, in real world, the distribution of data is usually far more complex and the assumption of Gaussian density with the same covariance is seldom to be met which greatl...

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...

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 ...