@INPROCEEDINGS{14.sam.pca,
   author =  "Nisrine Ghadban and Paul Honeine and Clovis Francis and Farah Mourad-Chehade and Joumana Farah",
   title =  "Strategies for principal component analysis in wireless sensor networks",
   booktitle =  "Proc. eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)",
   address =  "A Coruna, Spain",
   year  =  "2014",
   month =  "22 - 25~" # jun,
   keywords  =  "machine learning, wireless sensor networks",
   acronym =  "SAM",
   pages={233-236}, 
   url_link= "https://ieeexplore.ieee.org/document/6882383",
   url_paper   =  "http://honeine.fr/paul/publi/14.sam.pca.pdf",
   abstract={This paper deals with the issue of monitoring physical phenomena using wireless sensor networks. It provides principal component analysis for the time series of sensors' measurements. Without the need to compute the sample covariance matrix, we derive several in-network strategies to estimate the principal axis, including noncooperative and diffusion strategies. The performance of the proposed strategies is illustrated in the issue of monitoring gas diffusion.}, 
   keywords={covariance matrices, principal component analysis, time series, wireless sensor networks, principal component analysis, wireless sensor networks, time series, covariance matrix, diffusion strategy, noncooperative strategy, gas diffusion monitoring, Wireless sensor networks, Principal component analysis, Covariance matrices, Temperature measurement, Convergence, Time series analysis, Pollution measurement, Principal component analysis, wireless sensor network, adaptive learning, distributed processing}, 
   doi={10.1109/SAM.2014.6882383}, 
   ISSN={2151-870X}, 
}

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