DocumentCode
155609
Title
Diffusion strategies for in-network principal component analysis
Author
Ghadban, Nisrine ; Honeine, Paul ; Mourad-Chehade, Farah ; Francis, Clovis ; Farah, Joumana
Author_Institution
Inst. Charles Delaunay, Univ. de Technol. de Troyes, Troyes, France
fYear
2014
fDate
21-24 Sept. 2014
Firstpage
1
Lastpage
6
Abstract
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.
Keywords
covariance matrices; principal component analysis; unsupervised learning; cooperative diffusion-based strategy; covariance matrix; in-network principal component analysis; Convergence; Cost function; Covariance matrices; Eigenvalues and eigenfunctions; Principal component analysis; Time series analysis; Wireless sensor networks; Principal component analysis; adaptive learning; distributed processing; network;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning for Signal Processing (MLSP), 2014 IEEE International Workshop on
Conference_Location
Reims
Type
conf
DOI
10.1109/MLSP.2014.6958849
Filename
6958849
Link To Document