Sparse multiple kernel learning : Minimax rates with random projection
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | 106142 |
Journal / Publication | Journal of Statistical Planning and Inference |
Volume | 231 |
Online published | 27 Dec 2023 |
Publication status | Published - Jul 2024 |
Link(s)
Abstract
In kernel-based learning, the random projection method, also called random sketching, has been successfully used in kernel ridge regression to reduce the computational burden in the big data setting, and at the same time retain the minimax convergence rate. In this work, we consider its use in sparse multiple kernel learning problems where a closed-form optimizer is not available, which poses significant technical challenges, for which the existing results do not carry over directly. Even when random projection is not used, our risk bound improves on the existing results in several aspects. We also illustrate the use of random projection via some numerical examples. © 2023 Elsevier B.V.
Research Area(s)
- Convergence rate, Kernel method, Random projection, Reproducing kernel Hilbert space
Citation Format(s)
Sparse multiple kernel learning: Minimax rates with random projection. / Lu, Wenqi; Zhu, Zhongyi; Li, Rui et al.
In: Journal of Statistical Planning and Inference, Vol. 231, 106142, 07.2024.
In: Journal of Statistical Planning and Inference, Vol. 231, 106142, 07.2024.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review