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Distributed principal component analysis on networks via directed graphical models

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3 Author(s)
Zhaoshi Meng ; Univ. of Michigan, Ann Arbor, MI, USA ; Wiesel, A. ; Hero, A.O.

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.

Published in:

Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on

Date of Conference:

25-30 March 2012

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