Name
Abstract | ||
|---|---|---|
Linear discriminant analysis (LDA) is the most widely used supervised dimensionality reduction approach. After removing the null space of the total scatter matrix St via principal component analysis (PCA), the LDA algorithm can avoid the small sample size problem. Most existing supervised dimensionality reduction methods extract the principal component of data first, and then conduct LDA on it. Ho... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TNNLS.2019.2904701 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Principal component analysis,Dimensionality reduction,Feature extraction,Covariance matrices,Null space,Data mining,Australia | Dimensionality reduction,Pattern recognition,Computer science,Artificial intelligence,Linear discriminant analysis,Machine learning,Principal component analysis | Journal |
Volume | Issue | ISSN |
31 | 2 | 2162-237X |
Citations | PageRank | References |
3 | 0.37 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaowei Zhao | 1 | 26 | 9.65 |
| jun guo | 2 | 16 | 6.41 |
| Feiping Nie | 3 | 7061 | 309.42 |
| Ling Chen | 4 | 664 | 35.33 |
| Zhihui Li | 5 | 252 | 16.39 |
| Huaxiang Zhang | 6 | 436 | 56.32 |