| Full text | |
| Author(s): |
Total Authors: 2
|
| Affiliation: | [1] Univ Sao Paulo, Escola Politecn, BR-05508900 Sao Paulo - Brazil
Total Affiliations: 1
|
| Document type: | Journal article |
| Source: | IEEE SIGNAL PROCESSING LETTERS; v. 27, p. 1130-1134, 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
Kernel methods have been used to solve nonlinear problems in signal processing and machine learning. To avoid a too large growth of the dictionary, different sparsification techniques have emerged in the literature. In this letter, we propose a Gram-Schmidt-based sparsification technique for kernel dictionaries. It projects the mapped vectors by the kernel into a finite-dimensional subspace spanned by an orthonormal basis and can be used with any Mercer kernel. In particular, we apply it to a least-mean-square-type (LMS) algorithm. Simulation results show that the proposed algorithm presents a lower computational cost when compared to kernel LMS implemented with other sparsification techniques, while maintaining the performance. (AU) | |
| FAPESP's process: | 17/20378-9 - Adaptive filters and machine learning: applications on image, communications, and speech |
| Grantee: | Magno Teófilo Madeira da Silva |
| Support Opportunities: | Regular Research Grants |