Self-weighted Robust LDA for Multiclass Classification with Edge Classes

ACM Transactions on Intelligent Systems and Technology - Tập 12 Số 1 - Trang 1-19 - 2021
Caixia Yan1, Xiaojun Chang2, Minnan Luo1, Qinghua Zheng1, Xiaoqin Zhang3, Zhihui Li4, Feiping Nie5
1School of Electronic and Information Engineering, Xi’an Jiaotong University, Shaanxi, China
2Faculty of Information Technology, Monash University, Australia
3College of Computer Science and Artificial Intelligence, Wenzhou University, China
4Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
5Center for Optical Image Analysis and Learning, Northwestern Polytechnical University, Shaanxi, China

Tóm tắt

Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative features for multi-class classification. A vast majority of existing LDA algorithms are prone to be dominated by the class with very large deviation from the others, i.e., edge class, which occurs frequently in multi-class classification. First, the existence of edge classes often makes the total mean biased in the calculation of between-class scatter matrix. Second, the exploitation of ℓ2-norm based between-class distance criterion magnifies the extremely large distance corresponding to edge class. In this regard, a novel self-weighted robust LDA with ℓ2,1-norm based pairwise between-class distance criterion, called SWRLDA, is proposed for multi-class classification especially with edge classes. SWRLDA can automatically avoid the optimal mean calculation and simultaneously learn adaptive weights for each class pair without setting any additional parameter. An efficient re-weighted algorithm is exploited to derive the global optimum of the challenging ℓ2,1-norm maximization problem. The proposed SWRLDA is easy to implement and converges fast in practice. Extensive experiments demonstrate that SWRLDA performs favorably against other compared methods on both synthetic and real-world datasets while presenting superior computational efficiency in comparison with other techniques.

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ACM Transactions on Intelligent Systems and Technology

Tập 12 Số 1

1-19

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