Dual auto-regressive modelling approach to Gaussian process identification

Yiu-ming Cheung
{"title":"Dual auto-regressive modelling approach to Gaussian process identification","authors":"Yiu-ming Cheung","doi":"10.1109/ICME.2001.1237906","DOIUrl":null,"url":null,"abstract":"By modelling sources as a multivariate auto-regressive (AR) process, we have recently presented a dual AR modelling approach to identify temporal sources in independent component analysis (ICA) (Cheung et al. 2000, Cheung and Xu 1999 & 2001). However, our proposed existing algorithms for this approach are only suitable for the case that the residual term of the AR source process is non-Gaussian white noise. In this paper, we further study the Gaussian case, whereby a maximum-likelihood based algorithm is presented and experimentally demonstrated.","PeriodicalId":405589,"journal":{"name":"IEEE International Conference on Multimedia and Expo, 2001. ICME 2001.","volume":"216 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Conference on Multimedia and Expo, 2001. ICME 2001.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICME.2001.1237906","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

By modelling sources as a multivariate auto-regressive (AR) process, we have recently presented a dual AR modelling approach to identify temporal sources in independent component analysis (ICA) (Cheung et al. 2000, Cheung and Xu 1999 & 2001). However, our proposed existing algorithms for this approach are only suitable for the case that the residual term of the AR source process is non-Gaussian white noise. In this paper, we further study the Gaussian case, whereby a maximum-likelihood based algorithm is presented and experimentally demonstrated.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
高斯过程识别的双自回归建模方法
通过将源建模为多元自回归(AR)过程,我们最近提出了一种双AR建模方法,用于识别独立成分分析(ICA)中的时间源(张等人,2000年,张和Xu 1999年和2001年)。然而,我们提出的现有算法仅适用于AR源过程的残差项为非高斯白噪声的情况。在本文中,我们进一步研究了高斯情况,提出了一种基于最大似然的算法并进行了实验证明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
The ITEA project EUROPA, a software platform for digital CE appliances Speech bandwidth extension A music similarity function based on signal analysis A beat-pattern based error concealment scheme for music delivery with burst packet loss Analysis of cache efficiency in 2D wavelet transform
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1