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Orthogonal sparse linear discriminant analysis
International Journal of Systems Science, 2018 © 2018 Informa UK Limited, trading as Taylor & Francis Group. Linear discriminant analysis (LDA) is a linear feature extraction approach, and it has received much attention.
Zhonghua Liu, Jiexin Pu, Xiaohong Wang
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Conditional Linear Discriminant Analysis
18th International Conference on Pattern Recognition (ICPR'06), 2006Dimensionality reduction by means of linear discriminant analysis (LDA) can generally lead to considerable improvements in classification accuracy and computation time. However, in supervised, pixel-based, image segmentation, the limiting factor of LDA that it cannot extract more than K - 1 features (K the number of classes) often prevents successfully
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Boosting in Linear Discriminant Analysis
2000In recent years, together with bagging [5] and the random subspace method [15], boosting [6] became one of the most popular combining techniques that allows us to improve a weak classifier. Usually, boosting is applied to Decision Trees (DT's). In this paper, we study boosting in Linear Discriminant Analysis (LDA).
Marina Skurichina, Robert P. W. Duin
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DDoS discrimination by Linear Discriminant Analysis (LDA)
2012 International Conference on Computing, Networking and Communications (ICNC), 2012In this paper, we propose an effective approach with a supervised learning system based on Linear Discriminant Analysis (LDA) to discriminate legitimate traffic from DDoS attack traffic. Currently there is a wide outbreak of DDoS attacks that remain risky for the entire Internet.
Theerasak Thapngam +2 more
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Training Linear Discriminant Analysis in Linear Time
2008 IEEE 24th International Conference on Data Engineering, 2008Linear Discriminant Analysis (LDA) has been a popular method for extracting features which preserve class separability. It has been widely used in many fields of information processing, such as machine learning, data mining, information retrieval, and pattern recognition. However, the computation of LDA involves dense matrices eigen-decomposition which
Deng Cai 0001 +2 more
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Unequal Priors in Linear Discriminant Analysis
Journal of Classification, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Carmen van Meegen +2 more
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Bayes Optimality in Linear Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008We present an algorithm which provides the one-dimensional subspace where the Bayes error is minimized for the C class problem with homoscedastic Gaussian distributions. Our main result shows that the set of possible one-dimensional spaces v, for which the order of the projected class means is identical, defines a convex region with associated convex ...
Onur C. Hamsici, Aleix M. Martínez
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Probabilistic Linear Discriminant Analysis
2006Linear dimensionality reduction methods, such as LDA, are often used in object recognition for feature extraction, but do not address the problem of how to use these features for recognition. In this paper, we propose Probabilistic LDA, a generative probability model with which we can both extract the features and combine them for recognition.
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Polynomial linear discriminant analysis
The Journal of Supercomputing, 2023Ruisheng Ran +3 more
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A comment on “Laplacian linear discriminant analysis”
Pattern Recognition, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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