@INPROCEEDINGS{14.mlsp.pca,
   author =  "Nisrine Ghadban and Paul Honeine and Farah Mourad-Chehade and Clovis Francis and Joumana Farah",
   title =  "Diffusion Strategies For In-Network Principal Component Analysis",
   booktitle =  "Proc. 24th IEEE workshop on Machine Learning for Signal Processing (MLSP)",
   address =  "Reims, France",
   year  =  "2014",
   month =  "21 - 24~" # sep,
   pages = "1 - 6",
   acronym =  "MLSP",
   url_link= "https://ieeexplore.ieee.org/document/6958849",
   url_paper   =  "http://honeine.fr/paul/publi/14.mlsp.pca.pdf",
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, in-network principal component analysis, covariance matrix, cooperative diffusion-based strategy, Principal component analysis, Covariance matrices, Cost function, Wireless sensor networks, Time series analysis, Eigenvalues and eigenfunctions, Convergence, Principal component analysis, network, adaptive learning, distributed processing}, 
   doi={10.1109/MLSP.2014.6958849}, 
   ISSN={1551-2541}, 
}

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