NetDA: an R package for network-based discriminant analysis subject to multilabel classes
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Publication:2095778
DOI10.1155/2022/1041752zbMath1505.62002OpenAlexW4297539956MaRDI QIDQ2095778
Publication date: 15 November 2022
Published in: Journal of Probability and Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1155/2022/1041752
Software, source code, etc. for problems pertaining to statistics (62-04) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Probabilistic graphical models (62H22)
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- Regularized linear discriminant analysis and its application in microarrays
- Sparse inverse covariance estimation with the graphical lasso
- General sparse multi-class linear discriminant analysis
- Network linear discriminant analysis
- De-noising analysis of noisy data under mixed graphical models
- Multiclass analysis and prediction with network structured covariates
- Graph-based sparse linear discriminant analysis for high-dimensional classification
- Network-based discriminant analysis for multiclassification
- Penalized Classification using Fisher’s Linear Discriminant
- Model selection and estimation in the Gaussian graphical model
- Nonparametric discriminant analysis with network structures in predictor
- Analysis of noisy survival data with graphical proportional hazards measurement error models
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