Name
Abstract | ||
|---|---|---|
Optimizing the parameters of kernel methods is an unsolved problem. We report an experimental evaluation and a consideration of the parameter dependences of kernel mutual subspace method (KMS). The following KMS parameters are considered: Gaussian kernel parameters, the dimensionalities of dictionary and input subspaces, and the number of canonical angles. We evaluate the recognition accuracies of KMS through experiments performed using the ETH- 80 animal database. By searching exhaustively for optimal parameters, we obtain 100% recognition accuracy, and some experimental results suggest relationships between the dimensionality of subspaces and the degrees of freedom for the motion of objects. Such results imply that KMS achieves a high recognition rate for object recognition with optimized parameters. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-22819-3_37 | ACCV Workshops (2) |
Keywords | Field | DocType |
object recognition,following kms parameter,input subspaces,recognition accuracy,experimental evaluation,kernel mutual subspace method,kernel method,gaussian kernel parameter,high recognition rate | Kernel (linear algebra),Pattern recognition,Subspace topology,Principal angles,Computer science,Curse of dimensionality,Linear subspace,Artificial intelligence,Kernel method,Gaussian function,Cognitive neuroscience of visual object recognition | Conference |
Volume | ISSN | Citations |
6469 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hitoshi Sakano | 1 | 126 | 13.02 |
| Osamu Yamaguchi | 2 | 671 | 44.09 |
| Tomokazu Kawahara | 3 | 36 | 1.77 |
| Seiji Hotta | 4 | 6 | 4.98 |