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Adaptive Local Linear Discriminant Analysis
ACM Transactions on Knowledge Discovery from Data, 2020Dimensionality reduction plays a significant role in high-dimensional data processing, and Linear Discriminant Analysis (LDA) is a widely used supervised dimensionality reduction approach.
F. Nie +4 more
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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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A New Formulation of Linear Discriminant Analysis for Robust Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering, 2019Dimensionality reduction is a critical technology in the domain of pattern recognition, and linear discriminant analysis (LDA) is one of the most popular supervised dimensionality reduction methods.
Haifeng Zhao, Z. Wang, F. Nie
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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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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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Linear Discriminant Analysis and Transvariation
Journal of Classification, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Computational Statistics & Data Analysis, 2020
The classification of normally distributed data in a high-dimensional setting when variables are more numerous than observations is considered. Under the assumption that the inverse covariance matrices (the precision matrices) are the same over all ...
Le Thi Khuyen +3 more
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The classification of normally distributed data in a high-dimensional setting when variables are more numerous than observations is considered. Under the assumption that the inverse covariance matrices (the precision matrices) are the same over all ...
Le Thi Khuyen +3 more
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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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Maize seed classification using hyperspectral image coupled with multi-linear discriminant analysis
, 2019Seed purity is an important parameter for evaluating seed quality and can be effectively studied by seed classification. Hyperspectral images between 400 and 1000 nm were acquired for 1632 maize seeds (17 varieties) for classifying seed varieties ...
Chaoyang Xia +5 more
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