Results 231 to 240 of about 278,527 (254)
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2012
In this chapter we discuss another popular data mining algorithm that can be used for supervised or unsupervised learning. Linear Discriminant Analysis (LDA) was proposed by R. Fischer in 1936. It consists in finding the projection hyperplane that minimizes the interclass variance and maximizes the distance between the projected means of the classes ...
Petros Xanthopoulos +2 more
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In this chapter we discuss another popular data mining algorithm that can be used for supervised or unsupervised learning. Linear Discriminant Analysis (LDA) was proposed by R. Fischer in 1936. It consists in finding the projection hyperplane that minimizes the interclass variance and maximizes the distance between the projected means of the classes ...
Petros Xanthopoulos +2 more
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Neighborhood linear discriminant analysis
Pattern Recognition, 2022Abstract Linear Discriminant Analysis (LDA) assumes that all samples from the same class are independently and identically distributed (i.i.d.). LDA may fail in the cases where the assumption does not hold. Particularly when a class contains several clusters (or subclasses), LDA cannot correctly depict the internal structure as the scatter matrices ...
Fa Zhu, Junbin Gao, Jian Yang, Ning Ye
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2006
This chapter deals with issues related to linear discriminant analysis (LDA). In the introduction, we indicate some basic conceptions of LDA. Then, the definitions and notations related to LDA are discussed. Finally, the introduction to non-linear LDA and the chapter summary are given.
David Zhang, Xiao-Yuan Jing, Jian Yang
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This chapter deals with issues related to linear discriminant analysis (LDA). In the introduction, we indicate some basic conceptions of LDA. Then, the definitions and notations related to LDA are discussed. Finally, the introduction to non-linear LDA and the chapter summary are given.
David Zhang, Xiao-Yuan Jing, Jian Yang
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Linear Discriminant Analysis for Signatures
IEEE Transactions on Neural Networks, 2010We propose signature linear discriminant analysis (signature-LDA) as an extension of LDA that can be applied to signatures, which are known to be more informative representations of local image features than vector representations, such as visual word histograms.
Seungil, Huh, Donghun, Lee
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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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Network linear discriminant analysis
Computational Statistics & Data Analysis, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cai, Wei +4 more
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Nonstationary linear discriminant analysis
2017 51st Asilomar Conference on Signals, Systems, and Computers, 2017Changes in population distributions over time are common in many applications. However, the vast majority of statistical learning theory takes place under the assumption that all points in the training data are identically distributed (and independent), that is, non-stationarity of the data is disregarded. In this paper, a version of the classic Linear
Shuilian Xie +3 more
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Laplacian linear discriminant analysis
Pattern Recognition, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tang, Hong, Fang, Tao, Shi, Peng-Fei
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Linear boundary discriminant analysis
Pattern Recognition, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Na, Jin Hee +2 more
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Distributed linear discriminant analysis
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011Linear discriminant analysis (LDA) is a widely used feature extraction method for classification. We introduce distributed implementations of different versions of LDA, suitable for many real applications. Classical eigen-formulation, iterative optimization of the subspace, and regularized LDA can be asymptotically approximated by all the nodes through
Sergio Valcarcel Macua +2 more
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