Gram-Schmidt-Based Sparsification for Kernel Dictionary

Autor(es)

Bueno, Andre A. ^[1] ; Silva, Magno T. M. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Escola Politecn, BR-05508900 Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: IEEE SIGNAL PROCESSING LETTERS; v. 27, p. 1130-1134, 2020.
• Citações Web of Science: 0

Resumo

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)

• Processo FAPESP: 17/20378-9 - Filtros adaptativos e aprendizagem de máquina: aplicações em imagens, comunicações e voz
• Beneficiário: Magno Teófilo Madeira da Silva
• Modalidade de apoio: Auxílio à Pesquisa - Regular