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.

Link Paper doi abstract bibtex

Link Paper doi abstract bibtex

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.

@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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