@inproceedings{4802459d9d8b437bad6974d531d1ac3a,
title = "Distributed principal component analysis on networks via directed graphical models",
abstract = "We introduce an efficient algorithm for performing distributed principal component analysis (PCA) on directed Gaussian graphical models. By exploiting structured sparsity in the Cholesky factor of the inverse covariance (concentration) matrix, our proposed DDPCA algorithm computes global principal subspace estimation through local computation and message passing. We show significant performance and computation/communication advantages of DDPCA for online principal subspace estimation and distributed anomaly detection in real-world computer networks.",
keywords = "Graphical models, anomaly detection, distributed PCA, principal component analysis, subspace tracking",
author = "Zhaoshi Meng and Ami Wiesel and Hero, {Alfred O.}",
year = "2012",
doi = "10.1109/ICASSP.2012.6288518",
language = "אנגלית",
isbn = "9781467300469",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "2877--2880",
booktitle = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings",
note = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 ; Conference date: 25-03-2012 Through 30-03-2012",
}