.png){kind=icon}
.png){kind=icon}
Return
Density-oriented linear discriminant analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2021.115946.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The conventional Linear Discriminant Analysis (LDA) model has some challenges, such as sensitivity to the outlier, the singularity problem of the within-class scatter matrix, and Gaussian assumption of data within the same class. This paper proposes a robust LDA method that tries to solve the sensitivity to outliers and singularity problems. Specifically, we first use Bayesian risk to design the proposed method optimization problem. Then, the proposed Density-oriented LDA (DLDA) method used the data density as prior knowledge for robustness against outliers. The proposed method can classify non-linear and multi-mode distribution data sets. Furthermore, the proposed method can be employed for big data classification using the AdaBoost approach. Experimental results on synthetic and real data sets demonstrate the proposed DLDA method's superiority over other competing methods.
Keywords:
Linear discriminant analysis (LDA)
Outliers
Data density
Singularity problem
Adaboost
Robust LDA
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W