Results 251 to 260 of about 24,268,118 (293)

Laplacian linear discriminant analysis

Pattern Recognition, 2006
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Hong Tang, Tao Fang, Pengfei Shi
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Distributed linear discriminant analysis

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
Linear 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, Pavle Belanovic, Santiago Zazo   +2 more
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Geometric linear discriminant analysis

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2002
When it becomes necessary to reduce the complexity of a classifier, dimensionality reduction can be an effective way to address classifier complexity. Linear discriminant analysis (LDA) is one approach to dimensionality reduction that makes use of a linear transformation matrix.
Mark Ordowski, Gerard G. L. Meyer
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Transferable Linear Discriminant Analysis

IEEE Transactions on Neural Networks and Learning Systems, 2020
Linear discriminant analysis (LDA) has been widely used as the technique of feature exaction. However, LDA may be invalid to address the data from different domains. The reasons are as follows: 1) the distribution discrepancy of data may disturb the linear transformation matrix so that it cannot extract the most discriminative feature and 2) the ...
Na Han, Jigang Wu, Xiaozhao Fang, Jie Wen 0001, Shanhua Zhan, Shengli Xie, Xuelong Li 0001   +6 more
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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 ...
Makoto Sakai, Norihide Kitaoka, Seiichi Nakagawa   +2 more
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Separable linear discriminant analysis

Computational Statistics & Data Analysis, 2012
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Jianhua Zhao, Philip L. H. Yu, Lei Shi 0004, Shulan Li   +3 more
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Linear Discriminant Analysis and Transvariation

Journal of Classification, 2004
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Network linear discriminant analysis

Computational Statistics & Data Analysis, 2018
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Wei Cai, Guoyu Guan, Rui Pan 0004, Xuening Zhu, Hansheng Wang 0002   +4 more
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Nonstationary linear discriminant analysis

2017 51st Asilomar Conference on Signals, Systems, and Computers, 2017
Changes 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, Mahdi Imani, Edward R. Dougherty, Ulisses M. Braga-Neto   +3 more
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Linear boundary discriminant analysis

Pattern Recognition, 2010
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Jin Hee Na, Myoung Soo Park, Jin Young Choi 0002   +2 more
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