Journal of Shandong University(Engineering Science) ›› 2021, Vol. 51 ›› Issue (2): 34-46.doi: 10.6040/j.issn.1672-3961.0.2020.234

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Global and local multi-view multi-label learning with active three-way clustering

ZHU Changming, YUE Wen, WANG Panhong, SHEN Zhenyu, ZHOU Rigui   

  1. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
  • Published:2021-04-16

Abstract: In order to consider the uncertain belongingness relationship between instances and clusters and then extend the application scopes of global and local multi-view multi-label learning, an algorithm of global and local multi-view multi-label learning machine with active three-way clustering(GLMVML-ATC)was proposed. With the usage of active three-way clustering strategy, the belongingness of instances to a cluster depended on the probabilities of uncertain instances belonging to core regions. This made local label correlations more authentic, which enhanced the performances of multi-view multi-label learning machines further and accelerated their development. Experimental results validated that GLMVML-ATC improved the classification performances with 3% at least, while the added training time less than 7%. It was superior to the classical multi-view learning machines and multi-label learning machines.

Key words: three-way clustering, multi-view, multi-label, label correlation, global and local

CLC Number: 

  • TP391
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