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Transferable Linear Discriminant Analysis
IEEE Transactions on Neural Networks and Learning Systems, 2020Linear discriminant analysis (LDA) has been widely used as the technique of feature exaction. However, LDA may be invalid to address the data from different domains. The reasons are as follows: 1) the distribution discrepancy of data may disturb the linear transformation matrix so that it cannot extract the most discriminative feature and 2) the ...
Na Han +6 more
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Linear Discriminant Analysis and Transvariation
Journal of Classification, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Separable linear discriminant analysis
Computational Statistics & Data Analysis, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jianhua Zhao +3 more
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Geometric linear discriminant analysis
2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2002When it becomes necessary to reduce the complexity of a classifier, dimensionality reduction can be an effective way to address classifier complexity. Linear discriminant analysis (LDA) is one approach to dimensionality reduction that makes use of a linear transformation matrix.
Mark Ordowski, Gerard G. L. Meyer
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Power linear discriminant analysis
2007 9th International Symposium on Signal Processing and Its Applications, 2007Dimensionality reduction is one of the important preprocessing steps to handle high-dimensional data. Linear discriminant analysis (LDA) is a classical and popular approach for this purpose. LDA finds an optimal linear transformation, which maximizes the ratio of the variance in the between-class distance to the variance in the within-class distance ...
Makoto Sakai +2 more
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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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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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