Sciweavers

Share
ICASSP
2009
IEEE

Principal component analysis in decomposable Gaussian graphical models

10 years 1 months ago
Principal component analysis in decomposable Gaussian graphical models
We consider principal component analysis (PCA) in decomposable Gaussian graphical models. We exploit the prior information in these models in order to distribute its computation. For this purpose, we reformulate the problem in the sparse inverse covariance (concentration) domain and solve the global eigenvalue problem using a sequence of local eigenvalue problems in each of the cliques of the decomposable graph. We demonstrate the application of our methodology in the context of decentralized anomaly detection in the Abilene backbone network. Based on the topology of the network, we propose an approximate statistical graphical model and distribute the computation of PCA.
Ami Wiesel, Alfred O. Hero III
Added 17 Aug 2010
Updated 17 Aug 2010
Type Conference
Year 2009
Where ICASSP
Authors Ami Wiesel, Alfred O. Hero III
Comments (0)
books