TY - JOUR
T1 - RDFS-TDC
T2 - Robust discriminant feature selection based on improved trace difference criterion
AU - Yang, Libo
AU - Zhu, Dawei
AU - Liu, Xuemei
AU - Nie, Feiping
N1 - Publisher Copyright:
© 2025
PY - 2025/7
Y1 - 2025/7
N2 - Various discriminant feature selection models have been proposed that combine discriminant subspaces and sparse constraints. However, most scholars ignore the sensitivity of the discriminant criterion to outliers. In this study, we propose a robust discriminative feature selection method called RDFS-TDC. RDFS-TDC learns the optimal discriminative projection based on the trace-difference criterion, which provides good flexibility while avoiding singular matrices. Subsequently, the objective function was optimized using an iterative reweighting method, which reduced the impact of outliers on the discriminant subspace during the learning process. To satisfy different sparsity requirements, this study introduces the L2,p norm constraint to impose row sparsity on the projection matrix. RDFS-TDC obtained 87.05%, 94.68%, 84.82%, and 89.60% accuracies on YaleB, COIL20, CMUPIE, and FERET, respectively, and the misclassification error rate was 0.01%-3.32% lower compared to other methods. In addition, RDFS-TDC performed better on datasets with different scenarios compared to SDFS, WDFS, Fisher Score, DLSR, ReliefF, and RFS.
AB - Various discriminant feature selection models have been proposed that combine discriminant subspaces and sparse constraints. However, most scholars ignore the sensitivity of the discriminant criterion to outliers. In this study, we propose a robust discriminative feature selection method called RDFS-TDC. RDFS-TDC learns the optimal discriminative projection based on the trace-difference criterion, which provides good flexibility while avoiding singular matrices. Subsequently, the objective function was optimized using an iterative reweighting method, which reduced the impact of outliers on the discriminant subspace during the learning process. To satisfy different sparsity requirements, this study introduces the L2,p norm constraint to impose row sparsity on the projection matrix. RDFS-TDC obtained 87.05%, 94.68%, 84.82%, and 89.60% accuracies on YaleB, COIL20, CMUPIE, and FERET, respectively, and the misclassification error rate was 0.01%-3.32% lower compared to other methods. In addition, RDFS-TDC performed better on datasets with different scenarios compared to SDFS, WDFS, Fisher Score, DLSR, ReliefF, and RFS.
KW - Discriminant subspace learning
KW - Feature selection
KW - Iterative reweighted method
KW - Sparse regularization
KW - Supervised learning
KW - Trace difference criterion
UR - http://www.scopus.com/inward/record.url?scp=85217900235&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2025.121940
DO - 10.1016/j.ins.2025.121940
M3 - 文章
AN - SCOPUS:85217900235
SN - 0020-0255
VL - 705
JO - Information Sciences
JF - Information Sciences
M1 - 121940
ER -