Abstract
In order to effectively prevent accidents of special equipment, numerous management platforms utilize the multi-source data of special equipment to predict the safety state of equipment. However, there is still a lack of methods to deal with noise when fusing different data sources. This paper proposes a novel low-rank semi-supervised multi-view clustering for special equipment safety warning (LSMVC). Which achieves robust multi-view clustering by using low rank representation (LRR) to reduce the impact of noise. To solve this non-smooth optimization problem, we propose an optimization procedure based on the Alternating Direction Method of Multipliers. Finally, experiments are carried out on six real datasets including the Elevator dataset, which is collected from the actual work. The results show that the proposed clustering method can achieve better clustering performance than other clustering method.
Supported by National Natural Science Foundation of China Projects (U20A20228). Huzhou special equipment testing institute commissioned development projects (073–20201210-02).
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Zhang, F., Yin, H., Cheng, X., Du, W., Xu, H. (2021). LSMVC:Low-rank Semi-supervised Multi-view Clustering for Special Equipment Safety Warning. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13109. Springer, Cham. https://doi.org/10.1007/978-3-030-92270-2_1
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DOI: https://doi.org/10.1007/978-3-030-92270-2_1
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