Abstract
In real-world multi-view multi-label clustering and some classification tasks, instances and the corresponding clusters has at least three kinds of relationships, belong-to definitely, not belong-to definitely, and uncertain. Some learning machines consider only two of them, for example, belong-to definitely and not belong-to definitely. Moreover, three-way decision-based clustering (TDC) strategy is a good method to make the belongingness of instances to a cluster depend on the probabilities of uncertain instances belonging to core regions. Thus in our work, we take the notion of classical multi-view multi-label learning machines as the basic and introduce TDC so as to develop a multi-view and multi-label method with three-way decision-based clustering (MVML-TDC) and consider the relationships between instances and clusters. Experimental results validate that MVML-TDC achieves a better average performance and an acceptable running time.
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Acknowledgment
This work is supported by ‘Chenguang Program’ supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission under grant number 18CG54. Furthermore, this work is also sponsored by Project funded by China Postdoctoral Science Foundation under grant number 2019M651576, National Natural Science Foundation of China (CN) under grant number 61602296, Natural Science Foundation of Shanghai (CN) under grant number 16ZR1414500. The authors would like to thank their supports.
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Zhu, C., Ma, L., Wang, P., Miao, D. (2020). Multi-view and Multi-label Method with Three-Way Decision-Based Clustering. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12306. Springer, Cham. https://doi.org/10.1007/978-3-030-60639-8_6
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DOI: https://doi.org/10.1007/978-3-030-60639-8_6
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