Name
Abstract | ||
|---|---|---|
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. 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. Based on the topology of the network, we propose an approximate statistical graphical model and distribute the computation of PCA. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TSP.2009.2025806 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
local eigenvalue problem,decomposable principal component analysis,principal component analysis,principal component analysis.,approximate statistical graphical model,abilene backbone network,global eigenvalue problem,pca problem,pca computation,graphical models,decentralized anomaly detection,decomposable graph,decomposable gaussian graphical model,index terms—anomaly detection,ambient intelligence,indexing terms,computer networks,gaussian processes,spine,statistical graphics,anomaly detection,distributed computing,network topology,graph theory,graphical model | Anomaly detection,Gaussian process,Artificial intelligence,Eigenvalues and eigenvectors,Graph theory,Mathematical optimization,Sparse PCA,Algorithm,Statistical model,Graphical model,Mathematics,Principal component analysis,Machine learning | Journal |
Volume | Issue | ISSN |
57 | 11 | 1053-587X |
Citations | PageRank | References |
14 | 0.86 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ami Wiesel | 1 | 461 | 39.93 |
| Alfred O. Hero III | 2 | 2600 | 301.12 |