文章

Smith, B.L.; Scherer, W.T.; Conklin, J.H. Exploring imputation techniques for missing data in transportation management systems. Transport. Res. Record. J. Transport. Res. Board 2003, 1836, 132–142.

被如下文章引用：

• TITLE: Multi-Matrices Factorization with Application to Missing Sensor Data Imputation
• AUTHORS: Xiao-Yu Huang,Wubin Li,Kang Chen,Xian-Hong Xiang,Rong Pan,Lei Li,Wen-Xue Cai
• KEYWORDS: matrix factorization, sensor data, probabilistic graphical model, missing estimation
• JOURNAL NAME: Sensors DOI: 10.3390/s131115172 Sep 07, 2014
• 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 T j . 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 R dxt, where Rj？≈ U T (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.