Kernel-based methods have well-appreciated performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. To cope with this limitation, multi-kernel learning has gained popularity thanks to its flexibility in choosing kernels from a prescribed kernel dictionary. Leveraging the random feature approximation and its recent orthogonality-promoting variant, the present contribution develops an online multi-kernel learning scheme to infer the intended nonlinear function 'on the fly.' Performance analysis shows that the novel algorithm can afford sublinear regret. Numerical tests on real datasets are carried out to showcase the effectiveness of the proposed algorithms.
|Original language||English (US)|
|Title of host publication||2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Sep 10 2018|
|Event||2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada|
Duration: Apr 15 2018 → Apr 20 2018
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Other||2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018|
|Period||4/15/18 → 4/20/18|
Bibliographical noteFunding Information:
This work was supported by NSF 1500713, 1514056 and 1711471.
© 2018 IEEE.
- Multi-kernel learning
- Online learning
- Online regression
- Random features