Diffusion Strategies For In-Network Principal Component Analysis. Ghadban, N., Honeine, P., Mourad-Chehade, F., Francis, C., & Farah, J. In Proc. 24th IEEE workshop on Machine Learning for Signal Processing (MLSP), pages 1 - 6, Reims, France, 21 - 24~September, 2014.
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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.

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