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

Multi-Matrices Factorization with Application to Missing Sensor Data Imputation

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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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Abstract

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.
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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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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