Results 241 to 250 of about 278,527 (254)

Linear Discriminant Analysis

2013
Suppose we are given a learning set \(\mathcal{L}\) of multivariate observations (i.e., input values \(\mathfrak{R}^r\)), and suppose each observation is known to have come from one of K predefined classes having similar characteristics. These classes may be identified, for example, as species of plants, levels of credit worthiness of customers ...
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Power linear discriminant analysis

2007 9th International Symposium on Signal Processing and Its Applications, 2007
Dimensionality 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, null Norihide Kitaoka, null Seiichi Nakagawa   +2 more
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Linear Discriminant Analysis and Transvariation

Journal of Classification, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Tensor Linear Discriminant Analysis

2009
Linear 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, Fengxi Song, Yong Xu, Zhizhen Liang   +3 more
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Linear Discriminant Analysis

2021
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Linear discriminant analysis

2017
Ph. D.
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Linear Discriminant Analysis

2022
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ASSUMPTIONS IN LINEAR DISCRIMINANT ANALYSIS

The Lancet, 1971
P, Winkel, E, Juhl
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Fisher Discriminant Analysis (Linear Discriminant Analysis)

2004
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Linear discriminant analysis and discriminative log-linear modeling

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
D. Keysers, H. Ney
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