title: Multi-matrices factorization with application to missing sensor data imputation.
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, [formula in text]. 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.
authors.affiliation: Software Institute, Sun Yat-Sen University, Guangzhou 510275, China. echxy@scut.edu.cn.
authors.forename: Xiao-Yu
authors.initials: XY
authors.lastname: Huang
authors.forename: Wubin
authors.initials: W
authors.lastname: Li
authors.forename: Kang
authors.initials: K
authors.lastname: Chen
authors.forename: Xian-Hong
authors.initials: XH
authors.lastname: Xiang
authors.forename: Rong
authors.initials: R
authors.lastname: Pan
authors.forename: Lei
authors.initials: L
authors.lastname: Li
authors.forename: Wen-Xue
authors.initials: WX
authors.lastname: Cai