DIFFUSION STRATEGIES FOR IN-NETWORK PRINCIPAL COMPONENT ANALYSIS

被引：0

作者：

Ghadban, Nisrine ^{[1
,2
]}

Honeine, Paul ^{[1
]}

Mourad-Chehade, Farah ^{[1
]}

Francis, Clovis ^{[2
]}

Farah, Joumana ^{[3
]}

机构：

[1] Univ Technol Troyes, CNRS, Inst Charles Delaunay, Troyes, France

[2] Univ Libanaise, Fac Genie, Beirut, Lebanon

[3] Holy Spirit Univ Kaslik, Fac Engn, Dept Telecommun, Kaslik, Lebanon

来源：

2014 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) | 2014年

关键词：

Principal component analysis; network; adaptive learning; distributed processing; ADAPTATION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper deals with the principal component analysis in networks, where it is improper to compute the sample covariance matrix. To this end, we derive several in-network strategies to estimate the principal axes, including noncooperative and cooperative (diffusion-based) strategies. The performance of the proposed strategies is illustrated on diverse applications, including image processing and dimensionality reduction of time series in wireless sensor networks.

引用

页数：6

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