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Linear discriminant analysis guided by unsupervised ensemble learning
Information Sciences, 2019The high dimensionality and sparsity of data often increase the complexity of clustering; these factors occur simultaneously in unsupervised learning. Clustering and linear discriminant analysis (LDA) are methods to reduce the dimensionality and sparsity
Ping Deng +4 more
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A Gesture Recognition System Based on Time Domain Features and Linear Discriminant Analysis
IEEE Transactions on Cognitive and Developmental Systems, 2018Surface electromyogram (sEMG) signals have been used to control multifunctional prosthetic hands. Researchers usually focused on the use of several channels with sEMG signals to identify more gestures without limiting the number of sEMG sensors. However,
F. Duan, Xina Ren, Yikang Yang
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A comment on “Laplacian linear discriminant analysis”
Pattern Recognition, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Probabilistic Linear Discriminant Analysis
2006Linear 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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Polynomial linear discriminant analysis
The Journal of Supercomputing, 2023Ruisheng Ran +3 more
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Linear discriminant analysis and discriminative log-linear modeling
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004Daniel Keysers, Hermann Ney
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ASSUMPTIONS IN LINEAR DISCRIMINANT ANALYSIS
The Lancet, 1971P, Winkel, E, Juhl
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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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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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Linear discriminant analysis (LDA)
Multivariate Data Analysis on Matrix Manifolds, 2021X. Liu
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libPLS: An integrated library for partial least squares regression and linear discriminant analysis
, 2018Hong-Dong Li, Qingsong Xu, Yizeng Liang
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