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Distributed linear discriminant analysis
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011Linear discriminant analysis (LDA) is a widely used feature extraction method for classification. We introduce distributed implementations of different versions of LDA, suitable for many real applications. Classical eigen-formulation, iterative optimization of the subspace, and regularized LDA can be asymptotically approximated by all the nodes through
Sergio Valcarcel Macua +2 more
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Laplacian linear discriminant analysis
Pattern Recognition, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hong Tang, Tao Fang, Pengfei Shi
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Linear boundary discriminant analysis
Pattern Recognition, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jin Hee Na +2 more
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Linear discriminant analysis for speechreading
1998 IEEE Second Workshop on Multimedia Signal Processing (Cat. No.98EX175), 2002This paper investigates the use of Fisher-Rao (1965) linear discriminant analysis (LDA) as a means of visual feature extraction for hidden Markov model based automatic speechreading. For every video frame, a three-dimensional region of interest containing the speaker's mouth over a sequence of adjacent frames is lexicographically arranged into a data ...
Gerasimos Potamianos, Hans Peter Graf
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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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