Distributed Principal Component Analysis for Wireless Sensor Networks

Abstract: The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like compression, event detection, and event recognition. This technique is based on a linear transform where the sensor measurements are projected on a set of principal components. When sensor measurements are correlated, a small set of principal components can explain most of the measurements variability. This allows to significantly decrease the amount of radio communication and of energy consumption. In this paper, we show that the power iteration method can be distributed in a sensor network in order to compute an approximation of the principal components. The proposed implementation relies on an aggregation service, which has recently been shown to provide a suitable framework for distributing the computation of a linear transform within a sensor network. We also extend this previous work by providing a deta...

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Yann-Aël Le Borgne, Sylvain Raybaud, Gianluca Bontempi
• Keywords: CORR 2010, Education, Linear Transform, Principal Components, Sensor Network