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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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Book ChapterDOI
TL;DR: In experiments demonstrating the feasibility of human iris recognition at up to 10 m distance between subject and camera, database wide experiments reveal no performance degradation with distance, and minor performance degraded with, in order of increasing effect, time, capture angle, and eyewear.
Abstract: We describe experiments demonstrating the feasibility of human iris recognition at up to 10 m distance between subject and camera. The iris images of 250 subjects were captured with a telescope and infrared camera, while varying distance, capture angle, environmental lighting, and eyewear. Automatic iris localization and registration algorithms, in conjunction with a local correlation based matcher, were used to obtain a similarity score between gallery and probe images. Both the area under the receiver operating characteristic (ROC) curve and the Fisher Linear Discriminant were used to measure the distance between authentic and imposter distributions. Among variables studied, database wide experiments reveal no performance degradation with distance, and minor performance degradation with, in order of increasing effect, time (one month), capture angle, and eyewear.

125 citations

Journal ArticleDOI
Yang Li1, Xudong Wang1, Mei-Lin Luo1, Ke Li1, Xiao-Feng Yang2, Qi Guo1 
TL;DR: The experimental results indicate that the proposed MRBF-MPSO-SVM classification method outperforms competing techniques in terms of classification accuracy, and shows the effectiveness of the proposed method for classification of seizure epochs and seizure-free epochs.
Abstract: The automatic detection of epileptic seizures from electroencephalography (EEG) signals is crucial for the localization and classification of epileptic seizure activity. However, seizure processes are typically dynamic and nonstationary, and thus, distinguishing rhythmic discharges from nonstationary processes is one of the challenging problems. In this paper, an adaptive and localized time–frequency representation in EEG signals is proposed by means of multiscale radial basis functions (MRBF) and a modified particle swarm optimization (MPSO) to improve both time and frequency resolution simultaneously, which is a novel MRBF-MPSO framework of the time–frequency feature extraction for epileptic EEG signals. The dimensionality of extracted features can be greatly reduced by the principle component analysis algorithm before the most discriminative features selected are fed into a support vector machine (SVM) classifier with the radial basis function (RBF) in order to separate epileptic seizure from seizure-free EEG signals. The classification performance of the proposed method has been evaluated by using several state-of-art feature extraction algorithms and other five different classifiers like linear discriminant analysis, and logistic regression. The experimental results indicate that the proposed MRBF-MPSO-SVM classification method outperforms competing techniques in terms of classification accuracy, and shows the effectiveness of the proposed method for classification of seizure epochs and seizure-free epochs.

125 citations

Proceedings ArticleDOI
27 Jun 2016
TL;DR: A novel multi-view clustering method called Discriminatively Embedded K-Means (DEKM) is proposed, which embeds the synchronous learning of multiple discriminative subspaces into multi- view K- means clustering to construct a unified framework, and adaptively control the intercoordinations between these subspacing simultaneously.
Abstract: In real world applications, more and more data, for example, image/video data, are high dimensional and repre-sented by multiple views which describe different perspectives of the data. Efficiently clustering such data is a challenge. To address this problem, this paper proposes a novel multi-view clustering method called Discriminatively Embedded K-Means (DEKM), which embeds the synchronous learning of multiple discriminative subspaces into multi-view K-Means clustering to construct a unified framework, and adaptively control the intercoordinations between these subspaces simultaneously. In this framework, we firstly design a weighted multi-view Linear Discriminant Analysis (LDA), and then develop an unsupervised optimization scheme to alternatively learn the common clustering indicator, multiple discriminative subspaces and weights for heterogeneous features with convergence. Comprehensive evaluations on three benchmark datasets and comparisons with several state-of-the-art multi-view clustering algorithms demonstrate the superiority of the proposed work.

125 citations

Journal ArticleDOI
TL;DR: In this paper, an electronic nose was used for the detection of adulteration of virgin olive oil, consisting of 12 metal oxide semiconductor sensors, was used to generate a pattern of the volatile compounds present in the samples.

125 citations

Journal ArticleDOI
TL;DR: This paper undertakes an investigation of the effect of unequal variance-covariance matrices on Fisher's linear discriminant function when used for discrimination or risk estimation, and the behavior of this function is compared with the optimal quadratic form.
Abstract: This paper undertakes an investigation of the effect of unequal variance-covariance matrices on Fisher's linear discriminant function when used for discrimination or risk estimation. The behavior of this function is compared with the optimal quadratic form when the parameters of the two populations are assumed to be known. The problem is reduced to canonical form, and formulas are developed both for the correlation coefficient of the linear and quadratic functions, and for the probabilities of misclassification resulting from use of each of these functions. Numerical values of these measures are obtained for a number of cases in which one variance-covariance matrix is a multiple d of the other. These calculations are carried out for d equal to 0.1, 0.2, 0.5, 1, 2, 5, and 10; for T2, the squared linear distance between the two populations, equal to 0, 1, 2, 4, and 8; for 7r, the relative frequency of population 1, equal to 2, 3, and 6; and for p, the number of variables, equal to 1, 2, 6, and 10.

125 citations


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