Abstract
When we want to know an event we are concerned, it is likely that the collected information is incomplete which may severely affect the consequent analysis. In this paper, we focus on the event recovery problem that aims to discover missing historical information for a certain event based on the limited known information. We formulate an event as a two dimensional data matrix, which will be called the event matrix in this paper, and convert the original problem to matrix completion problem. We observe that the event matrix has low-rank structure due to the strong dependence between different event attributes. Then we adopt a recently proposed approach called Truncated Nuclear Norm Minimization (TNNM) to recover the event matrix. We also propose an early stopping strategy to further accelerate the optimization of TNNM. Experimental results on a collected event dataset demonstrate the effectiveness and the fast convergence rate of the proposed algorithm.
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Acknowledgements
This work was supported by National Basic Research Program of China (973 Program) under Grant 2012CB316404, National Program for Special Support of Top-Notch Young Professionals, and National Natural Science Foundation of China under Grant 61233011 and Grant 61125203.
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Zhang, D., Wei, L., Hong, B., Hu, Y., Cai, D., He, X. (2015). Event Recovery by Faster Truncated Nuclear Norm Minimization. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_17
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DOI: https://doi.org/10.1007/978-3-319-23862-3_17
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