Reducing Transmission Cost of Distributed Principal Components Analysis in Wireless Networks With Accuracy Guaranteed

Abstract

As a classic data processing tool, Principal Component Analysis (PCA) has been widely applied in various data analysis applications. To mitigate the high computational complexity of PCA on Big Data, distributed PCA methods have been extensively studied, which disperse the computational tasks across multiple computation units while guaranteeing the accuracy. For the scenarios of distributed PCA in wireless networks, as the data is originally dispersed across different locations, it is further required to reduce the communication cost of distributed PCA in networks, which however has been seldom studied. Reducing the communication cost of distributed PCA in wireless networks requires not only appropriately partitioning the computation of PCA, ensuring accuracy, but also effectively assigning the partitioned computations and routing strategies to the nodes. In this paper, we propose CD-PCA, a communication-efficient distributed PCA (CD-PCA) scheme. This scheme implements a transmission-benefit equipartition strategy for the network to facilitate high-accuracy distributed computation and designs novel routing strategies for nodes to execute the distributed PCA within each partitioned region. Extensive simulation results demonstrate that the proposed CD-PCA scheme can reduce transmission costs by over 30% on average compared to related methods and baseline approaches.