Article: Reports from University of Michigan highlight recent research in signal processing.(Report)

"In this paper, we consider principal component analysis (PCA) in decomposable Gaussian graphical models. We exploit the prior information in these models in order to distribute PCA computation," investigators in the United States report.

"For this purpose, we reformulate the PCA problem in the sparse inverse covariance (concentration) domain and address the global eigenvalue problem by solving a sequence of local eigenvalue problems in each of the cliques of the decomposable graph. We illustrate our methodology in the context of decentralized anomaly detection in the Abilene backbone network," wrote A. Wiesel and colleagues, University of Michigan.

The ...

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