Name
Abstract | ||
|---|---|---|
Subspace methods have been used widely for reduction capacity of memory or complexity of system and increasing classification performances in pattern recognition and signal processing. We propose a new subspace method based on a data generation model with intra-class factor and extra-class factor. The extra-class factor is associated with the distribution of classes and is important for discriminating classes. The intra-class factor is associated with the distribution within a class, and is required to be diminished for obtaining high class-separability. In the proposed method, we first estimate the intra-class factors and reduce them from the original data. We then extract the extra-class factors by PCA. For verification of proposed method, we conducted computational experiments on real facial data, and show that it gives better performance than conventional methods. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-69158-7_57 | ICONIP (1) |
Keywords | Field | DocType |
original data,subspace method,conventional method,new subspace method,data generation model,extra-class factor,real facial data,class information,better performance,intra-class factor,computer experiment,signal processing,pattern recognition | Signal processing,Facial recognition system,Data mining,Pattern recognition,Subspace topology,Computer science,Random subspace method,Artificial intelligence,Machine learning,Test data generation | Conference |
Volume | ISSN | Citations |
4984 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minkook Cho | 1 | 6 | 2.55 |
| Dongwoo Yoon | 2 | 0 | 0.34 |
| Hyeyoung Park | 3 | 194 | 32.70 |