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Regularized linear discriminant analysis based on generalized capped l2,q-norm.
- Source :
- Annals of Operations Research; Aug2024, Vol. 339 Issue 3, p1433-1459, 27p
- Publication Year :
- 2024
-
Abstract
- Aiming to improve the robustness and adaptiveness of the recently investigated capped norm linear discriminant analysis (CLDA), this paper proposes a regularized linear discriminant analysis based on the generalized capped l 2 , q -norm (GCLDA). Compared to CLDA, there are two improvements in GCLDA. Firstly, GCLDA uses the capped l 2 , q -norm rather than the capped l 2 , 1 -norm to measure the within-class and between-class distances for arbitrary q > 0 . By selecting an appropriate q, GCLDA is adaptive to different data, and also removes extreme outliers and suppresses the effect of noise more effectively. Secondly, by taking into account a regularization term, GCLDA not only improves its generalization ability but also avoids singularity. GCLDA is solved through a series of generalized eigenvalue problems. Experiments on an artificial dataset, some real world datasets and a high-dimensional dataset demonstrate the effectiveness of GCLDA. [ABSTRACT FROM AUTHOR]
- Subjects :
- FISHER discriminant analysis
EIGENVALUES
GENERALIZATION
Subjects
Details
- Language :
- English
- ISSN :
- 02545330
- Volume :
- 339
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Annals of Operations Research
- Publication Type :
- Academic Journal
- Accession number :
- 179039613
- Full Text :
- https://doi.org/10.1007/s10479-022-04959-y