Distributed clustering using collective principal component analysis
Knowledge and Information Systems
Diagnosing network-wide traffic anomalies
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
The Journal of Machine Learning Research
Principal component analysis for distributed data sets with updating
APPT'05 Proceedings of the 6th international conference on Advanced Parallel Processing Technologies
Optimal dimensionality reduction of sensor data in multisensor estimation fusion
IEEE Transactions on Signal Processing
Distributed Estimation Using Reduced-Dimensionality Sensor Observations
IEEE Transactions on Signal Processing
The Distributed Karhunen–Loève Transform
IEEE Transactions on Information Theory
Dimensionality Reduction for Distributed Estimation in the Infinite Dimensional Regime
IEEE Transactions on Information Theory
Covariance estimation in decomposable Gaussian graphical models
IEEE Transactions on Signal Processing
Hi-index | 35.69 |
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.