• Corpus ID: 226246428

Capped norm linear discriminant analysis and its applications

@article{Liu2020CappedNL,
  title={Capped norm linear discriminant analysis and its applications},
  author={Jiakou Liu and Xiong Xiong and Pei-Wei Ren and Da Zhao and Chunna Li and Yuanhai Shao},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.02147}
}
Classical linear discriminant analysis (LDA) is based on squared Frobenious norm and hence is sensitive to outliers and noise. To improve the robustness of LDA, in this paper, we introduce capped l_{2,1}-norm of a matrix, which employs non-squared l_2-norm and "capped" operation, and further propose a novel capped l_{2,1}-norm linear discriminant analysis, called CLDA. Due to the use of capped l_{2,1}-norm, CLDA can effectively remove extreme outliers and suppress the effect of noise data. In… 
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References

SHOWING 1-10 OF 59 REFERENCES
Linear Discriminant Analysis Based on L1-Norm Maximization
TLDR
This paper proposes a simple but effective robust LDA version based on L1-norm maximization, which learns a set of local optimal projection vectors by maximizing the ratio of the L2-norm-based between-class dispersion and the within- class dispersion.
Fisher Discriminant Analysis With L1-Norm
TLDR
A new method is proposed, termed LDA-L1, by maximizing the ratio of the between- class dispersion to the within-class dispersion using the L1-norm rather than the L2-norm, which is robust to outliers, and is solved by an iterative algorithm proposed.
Generalization of linear discriminant analysis using Lp-norm
Robust L1-norm two-dimensional linear discriminant analysis
L1-Norm Distance Linear Discriminant Analysis Based on an Effective Iterative Algorithm
TLDR
It is shown that due to the use of this strategy, L1-LDA is accompanied with some serious problems that hinder the derivation of the optimal discrimination for data, and an effective iterative framework to solve a general L 1-norm minimization–maximization (minmax) problem is proposed.
An Improved Linear Discriminant Analysis with L1-Norm for Robust Feature Extraction
TLDR
A novel algorithm termed as ILDA-L1 is developed, which can optimize all the discriminant vectors simultaneously in a unified framework and is confirmed to be effective in extracting robust features.
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