| Paper ID | MLSP-30.1 |
| Paper Title |
FAST GRAPH KERNEL WITH OPTICAL RANDOM FEATURES |
| Authors |
Hashem Ghanem, Nicolas Keriven, Nicolas Tremblay, GIPSA-lab, CNRS, UGA, Grenoble INP, France |
| Session | MLSP-30: Graph Signal Processing |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 14:00 - 14:45 |
| Presentation Time: | Thursday, 10 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-GKM] Graphical and kernel methods |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
The graphlet kernel is a classical method in graph classification. It however suffers from a high computation cost due to the isomorphism test it includes. As a generic proxy, and in general at the cost of losing some information, this test can be efficiently replaced by a user-defined mapping that computes various graph characteristics. In this paper, we propose to leverage kernel random features within the graphlet framework, and establish a theoretical link with a mean kernel metric. If this method can still be prohibitively costly for usual random features, we then incorporate optical random features that can be computed in constant time. Experiments show that the resulting algorithm is orders of magnitude faster than the graphlet kernel for the same, or better, accuracy. |
