.png){kind=icon}
返回
Gram-Schmidt-Based Sparsification for Kernel Dictionary
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2020.3004022.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Kernel-based-methods
dictionary sparsification
adaptive filtering
nonlinear signal processing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
.png){kind=icon}
The Onosmas of Linnaeus and Sibthorp, with a Note on those of Tournefort's Herbarium.林奈和西博恩的Onosmas，并附有关于图尔纳福尔植物标本馆中相关内容的注释。
PREAI
分享收藏Nonlinear adaptive filtering using kernel-based algorithms with dictionary adaptation使用具有字典自适应的基于核的算法进行非线性自适应滤波
OAAI
OAAI分享收藏
查看更多内容