Name
Title | ||
|---|---|---|
A new linear discriminant analysis algorithm based on L1-norm maximization and locality preserving projection. |
Abstract | ||
|---|---|---|
Generic L2-norm-based linear discriminant analysis (LDA) is sensitive to outliers and only captures global structure information of sample points. In this paper, a new LDA-based feature extraction algorithm is proposed to integrate both global and local structure information via a unified L1-norm optimization framework. Unlike generic L2-norm-based LDA, the proposed algorithm explicitly incorporates the local structure information of sample points and is robust to outliers. It overcomes the problem of the singularity of within-class scatter matrix as well. Experiments on several popular datasets demonstrate the effectiveness of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s10044-017-0594-y | Pattern Anal. Appl. |
Keywords | Field | DocType |
Feature extraction, Linear discriminant analysis, Local information, L1-norm, L2-norm, Pattern classification | Locality,Singularity,Artificial intelligence,Scatter matrix,Pattern recognition,Outlier,Algorithm,Feature extraction,Linear discriminant analysis,Norm (mathematics),Maximization,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
21 | 3 | 1433-755X |
Citations | PageRank | References |
0 | 0.34 | 32 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Zhang | 1 | 36 | 5.84 |
| Xueqiang Li | 2 | 47 | 4.54 |
| Jiazhong He | 3 | 36 | 6.14 |
| Minghui Du | 4 | 97 | 11.63 |