RDFS-TDC: Robust discriminant feature selection based on improved trace difference criterion

Libo Yang, Dawei Zhu, Xuemei Liu, Feiping Nie

科研成果: 期刊稿件文章同行评审

摘要

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.

源语言英语
文章编号121940
期刊Information Sciences
705
DOI
出版状态已出版 - 7月 2025

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