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Journal of Classification

Erschienen in:

14.10.2023

Multiclass Sparse Discriminant Analysis Incorporating Graphical Structure Among Predictors

verfasst von: Jingxuan Luo, Xuejiao Li, Chongxiu Yu, Gaorong Li

Erschienen in: Journal of Classification | Ausgabe 3/2023

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Abstract

In the era of big data, many sparse linear discriminant analysis methods have been proposed for classification and variable selection of the high-dimensional data. In order to solve the multiclass sparse discriminant problem for high-dimensional data under the Gaussian graphical model, this paper proposes a multiclass sparse discrimination analysis method by incorporating the graphical structure among predictors, which is named as IG-MSDA method. Our proposed IG-MSDA method can be used to estimate the vectors of all discriminant directions simultaneously. Under certain regularity conditions, it is shown that the proposed IG-MSDA method can consistently estimate all discriminant directions and the Bayes rule. Further, we establish the convergence rates of the estimators for the discriminant directions and the conditional misclassification rates. Finally, simulation studies and a real data analysis demonstrate the good performance of our proposed IG-MSDA method.

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Titel: Multiclass Sparse Discriminant Analysis Incorporating Graphical Structure Among Predictors
verfasst von: Jingxuan Luo
Xuejiao Li
Chongxiu Yu
Gaorong Li
Publikationsdatum: 14.10.2023
Verlag: Springer US
Erschienen in: Journal of Classification / Ausgabe 3/2023
Print ISSN: 0176-4268
Elektronische ISSN: 1432-1343
DOI: https://doi.org/10.1007/s00357-023-09451-1

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