Academy of Mathematics and Systems Science, CAS
Colloquia & Seminars

Speaker:	李高荣教授,北京师范大学
Inviter:
Title:	Multiclass Sparse Discriminant Analysis Incorporating Graphical Structure among Predictors
Time & Venue:	2022.11.17 14:20-15:10 腾讯会议：629-427-098
Abstract:	In the era of big data, many sparse linear discriminant analysis methods have been proposed for the 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 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.