Gossip algorithms for principal component analysis in networks
Résumé
This paper deals with the issues of the dimensionality reduction and the extraction of the structure of data using principal component analysis for the multivariable data in large-scale networks. In order to overcome the high computational complexity of this technique, we derive several in-network strategies to estimate the principal axes without the need for computing the sample covariance matrix. To this aim, we propose to combine Oja's iterative rule with average gossiping algorithms. Gossiping is used as a solution for communication between asynchronous nodes. The performance of the proposed approach is illustrated on time series acquisition in wireless sensor networks.
Mots clés
Signal processing algorithms
Algorithm design and analysis
Cost function
Routing
Signal processing
Data mining
Gossip averaging
in-network processing
adaptive learning
distributed processing
computational complexity
iterative methods
time series
wireless sensor networks
gossip algorithms
large-scale networks
Oja
iterative rule
asynchronous nodes
time series acquisition
Principal component analysis
| Origine | Fichiers produits par l'(les) auteur(s) |
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