SRMD:Sparse Random Mode Decomposition

作     者：Nicholas Richardson Hayden Schaeffer Giang Tran Nicholas Richardson;Hayden Schaeffer;Giang Tran

作者机构：University of WaterlooWaterlooONCanada University of British ColumbiaVancouverBCCanada University of CaliforniaLos AngelesCAUSA 

出 版 物：《Communications on Applied Mathematics and Computation》 (应用数学与计算数学学报（英文）)

年 卷 期：2024年第6卷第2期

页      面：879-906页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 070102[理学-计算数学] 0701[理学-数学] 

基　　金：supported in part by the NSERC RGPIN 50503-10842 supported in part by the AFOSR MURI FA9550-21-1-0084 the NSF DMS-1752116 

主　　题：Sparse random features Signal decomposition Short-time Fourier transform 

摘      要：Signal decomposition and multiscale signal analysis provide many useful tools for timefrequency *** proposed a random feature method for analyzing time-series data by constructing a sparse approximation to the *** randomization is both in the time window locations and the frequency sampling,which lowers the overall sampling and computational *** sparsification of the spectrogram leads to a sharp separation between time-frequency clusters which makes it easier to identify intrinsic modes,and thus leads to a new data-driven mode *** applications include signal representation,outlier removal,and mode *** benchmark tests,we show that our approach outperforms other state-of-the-art decomposition methods.