Name
Abstract | ||
|---|---|---|
A novel supervised learning method is proposed in this paper. It is an extension of local tangent space alignment (LTSA) to supervised feature extraction. First LTSA has been improved to be suitable in a changing, dynamic environment, that is, now it can map new data to the embedded low-dimensional space. Next class membership information is introduced to construct local tangent space when data sets contain multiple classes. This method has been applied to a number of data sets for classification and performs well when combined with some simple classifiers. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11427391_87 | ISNN (1) |
Keywords | Field | DocType |
next class membership information,supervised feature extraction,local tangent space,first ltsa,supervised learning,dynamic environment,new data,embedded low-dimensional space,multiple class,local tangent space alignment,feature extraction | Local tangent space alignment,Computer vision,Data set,Feature vector,Pattern recognition,Computer science,Feature extraction,Supervised learning,Artificial intelligence,Nonlinear manifold,Machine learning,Tangent space | Conference |
Volume | ISSN | ISBN |
3496 | 0302-9743 | 3-540-25912-0 |
Citations | PageRank | References |
10 | 0.88 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongyu Li | 1 | 443 | 32.34 |
| Li Teng | 2 | 45 | 3.98 |
| Wenbin Chen | 3 | 117 | 9.17 |
| I-Fan Shen | 4 | 41 | 5.24 |