The nonparametric Bayesian dictionary learning based interpolation method for WSNs missing data |
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| Affiliation: | 1. School of Information Engineering, East China Jiaotong University, Nanchang 330013, China;2. State Grid Nanchang City Honggutan Electric Power Supply Company, Nanchang 330013, China;1. Institute of Space Electronics and Information Technology, School of Electronic Science and Engineering, National University of Defense Technology, China;2. Department of Signal Processing and Acoustics, Aalto University, Finland;3. China Academy of Electronics and Information Technology, China Electronics Technology Group Corporation, China;1. National Research Center of Railway Safety Assessment, Beijing Jiaotong University, Beijing 100044, China;2. State Key Lab of Rail Traffic Control & Safety, Beijing Jiaotong University, Beijing 100044, China;1. College of Computer Science and Technology, Jilin University, Changchun 130012, China;2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;3. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta 30302, USA;4. College of Software, Jilin University, Changchun 130012, China |
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| Abstract: | The conventional data interpolation methods based on sparse representation usually assume that the signal is sparse under the overcomplete dictionary. Specially, they must confirm the dimensions of dictionary and the signal sparse level in advance. However, it is hard to know them if the signal is complicated or dynamically changing. In this paper, we proposed a nonparametric Bayesian dictionary learning based interpolation method for WSNs missing data, which is the combination of sparse representation and data interpolation. This method need not preset sparse degrees and dictionary dimensions, and our dictionary atoms are drawn from a multivariate normal distribution. In this case, the dictionary size will be learned adaptively by the nonparametric Bayesian method. In addition, we implement the Dirichlet process to exploit the spatial similarity of the sensing data in WSNs, thus to improve the interpolation accuracy. The interpolation model parameters, the optimal dictionary and sparse coefficients, can be inferred by the means of Gibbs sampling. The missing data will be estimated commendably through the derived parameters. The experimental results show that the data interpolation method we proposed outperforms the conventional methods in terms of interpolation accuracy and robustness. |
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| Keywords: | Data interpolation Nonparametric Bayesian Dictionary learning Dirichlet process |
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