Multi-Matrices Factorization with Application to Missing Sensor Data Imputation

We formulate a multi-matrices factorization model (MMF) for the missing sensor data estimation problem. The estimation problem is adequately transformed into a matrix completion one. With MMF, an n-by-t real matrix, R, is adopted to represent the data collected by mobile sensors from n areas at the...

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Bibliographic Details
Main Authors: Wen-Xue Cai, Rong Pan, Lei Li, Kang Chen, Xian-Hong Xiang, Wubin Li, Xiao-Yu Huang
Format: Article
Language:English
Published: MDPI AG 2013-11-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/13/11/15172
Description
Summary:We formulate a multi-matrices factorization model (MMF) for the missing sensor data estimation problem. The estimation problem is adequately transformed into a matrix completion one. With MMF, an n-by-t real matrix, R, is adopted to represent the data collected by mobile sensors from n areas at the time, T1, T2, ... , Tt, where the entry, Rij, is the aggregate value of the data collected in the ith area at Tj . We propose to approximate R by seeking a family of d-by-n probabilistic spatial feature matrices, U(1), U(2), ... , U(t), and a probabilistic temporal feature matrix, V E Rdxt, where Rj ≈ UT(j)Tj . We also present a solution algorithm to the proposed model. We evaluate MMF with synthetic data and a real-world sensor dataset extensively. Experimental results demonstrate that our approach outperforms the state-of-the-art comparison algorithms.
ISSN:1424-8220