In this paper we describe a face recognition method based on PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The method consists of two steps: rst we project the face image from the original vector space to a face subspace via PCA, second we use LDA to obtain a best linear clas-siier. The basic idea of combining PCA and LDA is to improve the generalization capability ...

It has been shown that the use of topic models for Information retrieval provides an increase in precision when used in the appropriate form. Latent Dirichlet Allocation (LDA) is a generative topic model that allows us to model documents using a Dirichlet prior. Using this topic model, we are able to obtain a fitted Dirichlet parameter that provides the maximum likelihood for the document set. ...

We calculate the electronic structure of several atoms and small molecules by direct minimization of the Self-Interaction Corrected Local Density Approximation (SIC-LDA) functional. To do this we first derive an expression for the gradient of this functional under the constraint that the orbitals be orthogonal and show that previously given expressions do not correctly incorporate this constrai...

Latent Dirichlet Allocation(LDA) is a popular topicmodel. Given the fact that the input corpus of LDA algorithms consists of millions to billions of tokens, the LDA training process is very time-consuming, which may prevent the usage of LDA in many scenarios, e.g., online service. GPUs have benefited modern machine learning algorithms and big data analysis as they can provide high memory bandwi...

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...