Gossip algorithms for principal component analysis in networks

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.