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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 ...
null Makoto Sakai +2 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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Tensor Linear Discriminant Analysis
2009Linear discriminant analysis is a very effective and important method for feature extraction. In general, image matrices are often transformed into vectors prior to feature extraction, which results in the curse of dimensionality when the dimensions of matrices are huge. In this chapter, classical LDA and its several variants are introduced.
David Zhang +3 more
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Integrative oncology: Addressing the global challenges of cancer prevention and treatment
Ca-A Cancer Journal for Clinicians, 2022Jun J Mao,, Msce +2 more
exaly
ASSUMPTIONS IN LINEAR DISCRIMINANT ANALYSIS
The Lancet, 1971P, Winkel, E, Juhl
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Multidisciplinary standards of care and recent progress in pancreatic ductal adenocarcinoma
Ca-A Cancer Journal for Clinicians, 2020Aaron J Grossberg +2 more
exaly