@INPROCEEDINGS{15.eusipco.gossip_pca,
   author =  "Nisrine Ghadban and Paul Honeine and Farah Mourad-Chehade and Joumana Farah and Clovis Francis",
   title =  "Gossip algorithms for principal component analysis in networks",
   booktitle =  "Proc. 23rd European Conference on Signal Processing (EUSIPCO)",
   address =  "Nice, France",
   year  =  "2015",
   month =  "31~" # aug # "--" # "4~" # sep,
   pages    =  "2366-2370",
   acronym =  "EUSIPCO",
   url_link= "https://ieeexplore.ieee.org/document/7362808",
   url_paper  =  "http://honeine.fr/paul/publi/15.eusipco.gossip_pca.pdf",
   abstract={This paper deals with the issues of the dimensionality reduction and the extraction of the structure of data using principal component analysis for the multivariable data in large-scale networks. In order to overcome the high computational complexity of this technique, we derive several in-network strategies to estimate the principal axes without the need for computing the sample covariance matrix. To this aim, we propose to combine Oja's iterative rule with average gossiping algorithms. Gossiping is used as a solution for communication between asynchronous nodes. The performance of the proposed approach is illustrated on time series acquisition in wireless sensor networks.}, 
   keywords={computational complexity, iterative methods, principal component analysis, time series, wireless sensor networks, gossip algorithms, principal component analysis, large-scale networks, computational complexity, Oja, iterative rule, asynchronous nodes, time series acquisition, wireless sensor networks, Principal component analysis, Signal processing algorithms, Algorithm design and analysis, Cost function, Routing, Signal processing, Data mining, Gossip averaging, principal component analysis, in-network processing, adaptive learning, distributed processing}, 
   doi={10.1109/EUSIPCO.2015.7362808}, 
   ISSN={2076-1465}, 
}

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