title: Distributed Density Estimation Based on a Mixture of Factor Analyzers in a Sensor Network.
abstract: Distributed density estimation in sensor networks has received much attention due to its broad applicability. When encountering high-dimensional observations, a mixture of factor analyzers (MFA) is taken to replace mixture of Gaussians for describing the distributions of observations. In this paper, we study distributed density estimation based on a mixture of factor analyzers. Existing estimation algorithms of the MFA are for the centralized case, which are not suitable for distributed processing in sensor networks. We present distributed density estimation algorithms for the MFA and its extension, the mixture of Student's t-factor analyzers (MtFA). We first define an objective function as the linear combination of local log-likelihoods. Then, we give the derivation process of the distributed estimation algorithms for the MFA and MtFA in details, respectively. In these algorithms, the local sufficient statistics (LSS) are calculated at first and diffused. Then, each node performs a linear combination of the received LSS from nodes in its neighborhood to obtain the combined sufficient statistics (CSS). Parameters of the MFA and the MtFA can be obtained by using the CSS. Finally, we evaluate the performance of these algorithms by numerical simulations and application example. Experimental results validate the promising performance of the proposed algorithms.
authors.affiliation: College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China. xwei@njupt.edu.cn.
authors.forename: Xin
authors.initials: X
authors.lastname: Wei
authors.affiliation: Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China. cgli@zju.edu.cn.
authors.forename: Chunguang
authors.initials: C
authors.lastname: Li
authors.affiliation: College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China. liang.zhou@njupt.edu.cn.
authors.forename: Liang
authors.initials: L
authors.lastname: Zhou
authors.affiliation: School of Information Science and Engineering, Southeast University, Nanjing 210096, China. zhaoli@seu.edu.cn.
authors.forename: Li
authors.initials: L
authors.lastname: Zhao
keywords: distributed density estimation; mixture of Student’s t-factor analyzers; mixture of factor analyzers; sensor network