Results 221 to 230 of about 1,506,457 (286)

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
openaire   +3 more sources

Laplacian linear discriminant analysis

Pattern Recognition, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hong Tang
exaly   +3 more sources

Network linear discriminant analysis

Computational Statistics & Data Analysis, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wei Cai, Guoyu Guan, Rui Pan 0004, Xuening Zhu, Hansheng Wang 0002   +4 more
openaire   +2 more sources

Linear Discriminant Analysis

Information Fusion and Data Science, 2020
Please download the sample Excel files from https://github.com/hhohho/Learn-Data-Mining-through-Excel for this chapter’s exercises.
Haitao Zhao, Zhihui Lai, H. Leung, Xianyi Zhang   +3 more
semanticscholar   +3 more sources

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data.

Journal of Visualized Experiments, 2022
There is growing attention toward closed biological genomes in the environment and in health. To explore and reveal the intergroup differences among different samples or environments, it is crucial to discover biomarkers with statistical differences ...
Fang Chang, Shishi He, Chenyuan Dang
semanticscholar   +1 more source

Multiclass diagnosis of stages of Alzheimer's disease using linear discriminant analysis scoring for multimodal data

Comput. Biol. Medicine, 2021
Alzheimer's disease (AD) is a progressive neurodegenerative disease, and mild cognitive impairment (MCI) is a transitional stage between normal control (NC) and AD.
Weiming Lin, Qinquan Gao, Min Du, Weisheng Chen, T. Tong   +4 more
semanticscholar   +1 more source

Linear Discriminant Analysis for Signatures

IEEE Transactions on Neural Networks, 2010
We 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.
S. Huh, D. Lee
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +1 more source

Cost-sensitive dual-bidirectional linear discriminant analysis

Information Sciences, 2020
In most previous cost-sensitive feature extraction methods, the image matrix needs to be converted into vectors. The conversion always leads to a high computation complexity and small sample size problem.
Huaxiong Li, Libo Zhang, Bing Huang, Xianzhong Zhou   +3 more
semanticscholar   +1 more source