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A Unified Framework of Random Feature KLMS Algorithms and Convergence Analysis

  • Xi'an Jiaotong University

科研成果: 书/报告/会议事项章节会议稿件同行评审

5 引用 (Scopus)

摘要

Random feature kernel least mean square RF KLMS) algorithms, like the random Fourier feature KLMS (RFF-KLMS), can effectively reduce the computation and storage burdens of the KLMS algorithm in the process of update. However, little work has been done to perform the convergence analysis for such algorithms. To this end, in this paper, we present a unified framework of RF-KLMS algorithms, and based on which, a universal model for convergence analysis is given. As two examples, the RFF-KLMS and the random Gaussian feature KLMS (RGF-KLMS) are discussed detailedly. Simulations demonstrate the validity of the theoretical analysis. Index Terms-Kernel least mean square, random feature, universal model, convergence analysis.

源语言英语
主期刊名2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9781509060146
DOI
出版状态已出版 - 10 10月 2018
活动2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, 巴西
期限: 8 7月 201813 7月 2018

出版系列

姓名Proceedings of the International Joint Conference on Neural Networks
2018-July

会议

会议2018 International Joint Conference on Neural Networks, IJCNN 2018
国家/地区巴西
Rio de Janeiro
时期8/07/1813/07/18

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