Name
Abstract | ||
|---|---|---|
Linear Discriminant Analysis (LDA) is a well-known method for fea- ture extraction and dimension reduction. It has been used widely in many applications such as face recognition. Recently, a novel LDA algo- rithm based on QR Decomposition, namely LDA/QR, has been proposed, which is competitive in terms of classification accuracy with other LDA algorithms, but it has much lower costs in time and space. However, LDA/QR is based on linear projection, which may not be suitable for data with nonlinear structure. This paper first proposes an algorithm called KDA/QR, which extends the LDA/QR algorithm to deal with nonlin- ear data by using the kernel operator. Then an efficient approximation of KDA/QR called AKDA/QR is proposed. Experiments on face image data show that the classification accuracy of both KDA/QR and AKDA/QR are competitive with Generalized Discriminant Analysis (GDA), a gen- eral kernel discriminant analysis algorithm, while AKDA/QR has much lower time and space costs. |
Year | Venue | Keywords |
|---|---|---|
2004 | NIPS | qr decomposition,dimension reduction,kernel discriminant analysis,face recognition,generalized discriminant analysis |
Field | DocType | Citations |
Kernel (linear algebra),Facial recognition system,Dimensionality reduction,Pattern recognition,Computer science,Kernel Fisher discriminant analysis,Feature extraction,Artificial intelligence,Linear discriminant analysis,QR decomposition,Machine learning,QR algorithm | Conference | 37 |
PageRank | References | Authors |
1.62 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Xiong | 1 | 293 | 14.90 |
| Jieping Ye | 2 | 6943 | 351.37 |
| Qi Li | 3 | 1170 | 66.43 |
| Ravi Janardan | 4 | 1241 | 121.04 |
| Vladimir Cherkassky | 5 | 1064 | 126.66 |