Topic
Linear discriminant analysis
About: Linear discriminant analysis is a research topic. Over the lifetime, 18361 publications have been published within this topic receiving 603195 citations. The topic is also known as: Linear discriminant analysis & LDA.
Papers published on a yearly basis
Papers
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TL;DR: A novel method that combines principal component analysis (PCA) and Fisher’s linear discriminant (FLD) method to show that the hybrid PCA-FLD method maximizes the representation and classification effects on the extracted new feature bands is presented.
Abstract: High-resolution hyperspectral imaging (HSI) provides an abundance of spectral data for feature analysis in image
processing. Usually, the amount of information contained in hyperspectral images is excessive and redundant, and data mining
for waveband selection is needed. In applications such as fruit and vegetable defect inspections, effective spectral combination
and data fusing methods are required in order to select a few optimal wavelengths without losing the crucial
information in the original hyperspectral data. In this article, we present a novel method that combines principal component
analysis (PCA) and Fisher’s linear discriminant (FLD) method to show that the hybrid PCA-FLD method maximizes the representation
and classification effects on the extracted new feature bands. The method is applied to the detection of chilling
injury on cucumbers. Based on tests on different types of samples, results show that this new integrated PCA-FLD method
outperforms the PCA and FLD methods when they are used separately for classifications. This method adds a new tool for
the multivariate analysis of hyperspectral images and can be extended to other hyperspectral imaging applications for fruit
and vegetable safety and quality inspections.
151 citations
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TL;DR: In this article, two approaches are described to aid the selection of the most appropriate procurement arrangements for a building project, one based on a multi-attribute technique and the other by means of discriminant analysis.
Abstract: Two approaches are described, which aid the selection of the most appropriate procurement arrangements for a building project. The first is a multi-attribute technique based on the National Economic Development Office procurement path decision chart. A small study is described in which the utility factors involved were weighted by averaging the scores of five 'experts' for three hypothetical building projects.
A concordance analysis is used to provide some evidence of any abnormal data sources. When applied to the study data, one of the experts was seen to be atypical.
The second approach is by means of discriminant analysis. This was found to provide reasonably consistent predictions through three discriminant functions. The analysis also showed the quality criteria to have no significant impact on the decision process.
Both approaches provided identical and intuitively correct answers in the study described. Some concluding remarks are made on the potential of discriminant analysis for future research and development in procurement selection techniques.
151 citations
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26 Dec 2007TL;DR: A novel face recognition method which exploits both global and local discriminative features, and which encodes the holistic facial information, such as facial contour, is proposed.
Abstract: In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which combines both global and local discriminative features. In this method, global features are extracted from whole face images by Fourier transform and local features are extracted from some spatially partitioned image patches by Gabor wavelet transform. After this, multiple classifiers are obtained by applying Fisher Discriminant Analysis on global Fourier features and local patches of Gabor features. All these classifiers are combined to form a hierarchical ensemble by sum rule. We evaluated the proposed method using Face Recognition Grand Challenge (FRGC) experimental protocols and database known as the largest data sets available. Experimental results on FRGC version 2.0 data set have shown that the proposed method achieves a verification rate of 86%, while the best reported was 76%.
150 citations
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27 Jun 2004TL;DR: By analyzing different overfitting problems for the two kinds of LDA classifiers, an approach using random subspace and bagging to improve them respectively is proposed and a robust face recognition system integrating shape, texture and Gabor responses is finally developed.
Abstract: Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, It often suffers from the small sample size problem when dealing with the high dimensional face data. Fisherface and null space LDA (N-LDA) are two conventional approaches to address this problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. In this paper, by analyzing different overfitting problems for the two kinds of LDA classifiers, we propose an approach using random subspace and bagging to improve them respectively. By random sampling on feature vector and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed. The two kinds of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. We also apply this approach to the integration of multiple features. A robust face recognition system integrating shape, texture and Gabor responses is finally developed.
150 citations
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28 Oct 2007TL;DR: By using spectral graph analysis, SRKDA casts discriminant analysis into a regression framework which facilitates both efficient computation and the use of regularization techniques, which is a huge save of computational cost.
Abstract: Linear discriminant analysis (LDA) has been a popular method for extracting features which preserve class separability. The projection vectors are commonly obtained by maximizing the between class covariance and simultaneously minimizing the within class covariance. LDA can be performed either in the original input space or in the reproducing kernel Hilbert space (RKHS) into which data points are mapped, which leads to Kernel Discriminant Analysis (KDA). When the data are highly nonlinear distributed, KDA can achieve better performance than LDA. However, computing the projective functions in KDA involves eigen-decomposition of kernel matrix, which is very expensive when a large number of training samples exist. In this paper, we present a new algorithm for kernel discriminant analysis, called spectral regression kernel discriminant analysis (SRKDA). By using spectral graph analysis, SRKDA casts discriminant analysis into a regression framework which facilitates both efficient computation and the use of regularization techniques. Specifically, SRKDA only needs to solve a set of regularized regression problems and there is no eigenvector computation involved, which is a huge save of computational cost. Our computational analysis shows that SRKDA is 27 times faster than the ordinary KDA. Moreover, the new formulation makes it very easy to develop incremental version of the algorithm which can fully utilize the computational results of the existing training samples. Experiments on face recognition demonstrate the effectiveness and efficiency of the proposed algorithm.
150 citations