Multi-view clustering via robust consistent graph learning

Wang, Changpeng; Li, Geng; Zhang, Jiangshe; Wu, Tianjun

doi:10.1016/j.dsp.2022.103607

Cited by 7 publications

(1 citation statement)

References 41 publications

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“…For a certain X v in the above multi-view dataset, its local structure information can be obtained by adaptive graph learning [27][28][29]. Considering each sample as a node on the graph, the connection probability of two nodes is the similarity between them.…”

Section: Adaptive Graph Learningmentioning

confidence: 99%

Tensorial multi-view subspace clustering with side constraints for elevator security warning

Xu,

Ruan,

et al. 2024

Multimedia Systems

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In recent years, using clustering technology to realize equipment security warning is a research hotspot in the field of data mining applications. However, due to the lack of data fusion mechanism and prior knowledge guidance, the performance of most existing methods is limited when applied to complex equipment such as elevator. In this paper, a novel Tensorial Multi-view Subspace Clustering with Side constraints (TMSCS) is proposed for elevator security warning, which first introduces tensorial multi-view subspace learning to achieve data fusion based on high-order correlation. Secondly, the prior knowledge is formalized as side constraints between samples through adaptive graph learning, where certain elevators is forced to have similar or dissimilar operation status. Thirdly, a unified model combining tensorial multi-view subspace learning and adaptive graph learning is constructed to eliminate the instability caused by phased learning. Furthermore, an efficient optimization algorithm is designed to solve this model. Extensive experiments on several benchmark datasets demonstrate the superiority of our method, and the experimental results on real elevator status datasets demonstrate that our method accurately identifies the operating status of each elevator equipment.

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Section: Adaptive Graph Learningmentioning

confidence: 99%