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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
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Journal ArticleDOI
01 Aug 2017
TL;DR: Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.
Abstract: Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs and raw EEG are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.

152 citations

Journal ArticleDOI
TL;DR: A classification approach that hybridizes statistical techniques and SOM for network anomaly detection and Probabilistic Self-Organizing Maps (PSOM) aim to model the feature space and enable distinguishing between normal and anomalous connections.

151 citations

Journal ArticleDOI
01 Jun 2010
TL;DR: The objective is to regulate the LPP space in a parametric manner and extract useful discriminant information from the whole feature space rather than a reduced projection subspace of principal component analysis which results in better locality preserving power and higher recognition accuracy than the original LPP method.
Abstract: We propose in this paper a parametric regularized locality preserving projections (LPP) method for face recognition. Our objective is to regulate the LPP space in a parametric manner and extract useful discriminant information from the whole feature space rather than a reduced projection subspace of principal component analysis. This results in better locality preserving power and higher recognition accuracy than the original LPP method. Moreover, the proposed regularization method can easily be extended to other manifold learning algorithms and to effectively address the small sample size problem. Experimental results on two widely used face databases demonstrate the efficacy of the proposed method.

151 citations

Journal ArticleDOI
TL;DR: A novel family of mixture models wherein each component is modeled using a multivariate t-distribution with an eigen-decomposed covariance structure is put forth, known as the tEIGEN family.
Abstract: The last decade has seen an explosion of work on the use of mixture models for clustering The use of the Gaussian mixture model has been common practice, with constraints sometimes imposed upon the component covariance matrices to give families of mixture models Similar approaches have also been applied, albeit with less fecundity, to classification and discriminant analysis In this paper, we begin with an introduction to model-based clustering and a succinct account of the state-of-the-art We then put forth a novel family of mixture models wherein each component is modeled using a multivariate t-distribution with an eigen-decomposed covariance structure This family, which is largely a t-analogue of the well-known MCLUST family, is known as the tEIGEN family The efficacy of this family for clustering, classification, and discriminant analysis is illustrated with both real and simulated data The performance of this family is compared to its Gaussian counterpart on three real data sets

151 citations

Journal ArticleDOI
TL;DR: A complete computer aided diagnosis (CAD) system for an automatic evaluation of the neuroimages is presented and principal component analysis (PCA)-based methods are proposed as feature extraction techniques, enhanced by other linear approaches such as linear discriminant analysis (LDA) or the measure of the Fisher discriminant ratio (FDR) for feature selection.

151 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20251
20242
2023756
20221,711
2021678
2020815