{"title":"基于Cholesky因子的小波变换域LMF算法","authors":"M. Moinuddin, A. Zerguine","doi":"10.1109/IEEEGCC.2006.5686209","DOIUrl":null,"url":null,"abstract":"This paper presents a new wavelet transform domain least mean fourth (LMF) algorithm. The algorithm exploits the special sparse structure of the wavelet transform of wide classes of correlation matrices and their Cholesky factors in order to compute a whitening transformation of the input data in the wavelet domain and minimize computational complexity. This method explicitly computes a sparse estimate of the wavelet domain correlation matrix of the input process. It then computes the Cholesky factor of that matrix and uses its inverse to whiten the input. The proposed algorithm has faster convergence rate than that of wavelet transform domain least mean square (LMS) algorithm.","PeriodicalId":433452,"journal":{"name":"2006 IEEE GCC Conference (GCC)","volume":"203 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cholesky factors based wavelet transform domain LMF algorithm\",\"authors\":\"M. Moinuddin, A. Zerguine\",\"doi\":\"10.1109/IEEEGCC.2006.5686209\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a new wavelet transform domain least mean fourth (LMF) algorithm. The algorithm exploits the special sparse structure of the wavelet transform of wide classes of correlation matrices and their Cholesky factors in order to compute a whitening transformation of the input data in the wavelet domain and minimize computational complexity. This method explicitly computes a sparse estimate of the wavelet domain correlation matrix of the input process. It then computes the Cholesky factor of that matrix and uses its inverse to whiten the input. The proposed algorithm has faster convergence rate than that of wavelet transform domain least mean square (LMS) algorithm.\",\"PeriodicalId\":433452,\"journal\":{\"name\":\"2006 IEEE GCC Conference (GCC)\",\"volume\":\"203 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-03-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE GCC Conference (GCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEEEGCC.2006.5686209\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE GCC Conference (GCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEEEGCC.2006.5686209","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cholesky factors based wavelet transform domain LMF algorithm
This paper presents a new wavelet transform domain least mean fourth (LMF) algorithm. The algorithm exploits the special sparse structure of the wavelet transform of wide classes of correlation matrices and their Cholesky factors in order to compute a whitening transformation of the input data in the wavelet domain and minimize computational complexity. This method explicitly computes a sparse estimate of the wavelet domain correlation matrix of the input process. It then computes the Cholesky factor of that matrix and uses its inverse to whiten the input. The proposed algorithm has faster convergence rate than that of wavelet transform domain least mean square (LMS) algorithm.
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