Results 241 to 250 of about 282,422 (272)

Training Linear Discriminant Analysis in Linear Time

2008 IEEE 24th International Conference on Data Engineering, 2008
Linear 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, Xiaofei He 0001, Jiawei Han 0001   +2 more
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Conditional Linear Discriminant Analysis

18th International Conference on Pattern Recognition (ICPR'06), 2006
Dimensionality 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

2000
In 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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Unequal Priors in Linear Discriminant Analysis

Journal of Classification, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Carmen van Meegen, Sarah Schnackenberg, Uwe Ligges   +2 more
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Probabilistic Linear Discriminant Analysis

2006
Linear dimensionality reduction methods, such as LDA, are often used in object recognition for feature extraction, but do not address the problem of how to use these features for recognition. In this paper, we propose Probabilistic LDA, a generative probability model with which we can both extract the features and combine them for recognition.
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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
Daniel Keysers, Hermann Ney
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ASSUMPTIONS IN LINEAR DISCRIMINANT ANALYSIS

The Lancet, 1971
P, Winkel, E, Juhl
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A comment on “Laplacian linear discriminant analysis”

Pattern Recognition, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Polynomial linear discriminant analysis

The Journal of Supercomputing, 2023
Ruisheng Ran, Ting Wang, Zheng Li, Bin Fang 0001   +3 more
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Linear Discriminant Analysis

2018
Linear discriminant analysis (LDA) is most commonly used as a dimensionality reduction technique in the pre-processing step for pattern classification and machine learning applications. In contrast to principal component analysis (PCA), LDA is “supervised” and computes the directions or linear discriminants that will represent the axes that maximize ...
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