Name
Abstract | ||
|---|---|---|
Slow feature analysis (SFA) is a new method based on the slowness principle and extracts slowly varying signals out of the input data. However, traditional SFA cannot be directly performed on those dataset without an obvious temporal structure. In this paper, a novel supervised slow feature analysis (SSFA) is proposed, which constructs pseudo-time series by taking advantage of the consensus information. Extensive experiments on AR and PIE face databases demonstrate superiority of our proposed method. © Springer International Publishing 2013. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-319-02961-0_22 | CCBR |
Keywords | Field | DocType |
consensus information,face recognition,slow feature analysis | Facial recognition system,Pattern recognition,Computer science,Artificial intelligence,Slowness,Pattern recognition (psychology) | Conference |
Volume | Issue | ISSN |
8232 LNCS | null | 16113349 |
Citations | PageRank | References |
3 | 0.37 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xingjian Gu | 1 | 68 | 5.05 |
| Chuancai Liu | 2 | 162 | 18.87 |
| Sheng Wang | 3 | 12 | 5.32 |