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Sensors 2013, 13(11), 15172-15186; doi:10.3390/s131115172

Multi-Matrices Factorization with Application to Missing Sensor Data Imputation

Software Institute, Sun Yat-Sen University, Guangzhou 510275, China
School of Economics and Commerce, South China University of Technology, Guangzhou 510006, China
Department of Computing Science, Umeå University, SE-901 87 Umeå, Sweden
Academy of Guangdong Telecom Co.Ltd, Guangzhou 510630, China
Department of Interventional Radiology, the First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
Author to whom correspondence should be addressed.
Received: 7 October 2013 / Revised: 28 October 2013 / Accepted: 28 October 2013 / Published: 6 November 2013
(This article belongs to the Section Physical Sensors)
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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. View Full-Text
Keywords: matrix factorization; sensor data; probabilistic graphical model; missing estimation matrix factorization; sensor data; probabilistic graphical model; missing estimation

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Huang, X.-Y.; Li, W.; Chen, K.; Xiang, X.-H.; Pan, R.; Li, L.; Cai, W.-X. Multi-Matrices Factorization with Application to Missing Sensor Data Imputation. Sensors 2013, 13, 15172-15186.

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