Journal of Guangdong University of Technology ›› 2021, Vol. 38 ›› Issue (03): 22-28,47.doi: 10.12052/gdutxb.200120

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A Semi-supervised Two-view Multiple-Instance Clustering Model

Cai Hao, Liu Bo   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2020-09-17 Online:2021-05-10 Published:2021-03-12

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Abstract: A novel semi-supervised two-view multi-instance clustering model is proposed, which bands text-view with image-view and solves the multi-instance image clustering problem with a small amount of label. Firstly, the proposed model embeds Concept Factorization and multi-instance kernel into a joint framework, which learns the association matrix of each view and the cluster indicator matrix shared by both views. Then, a ${l_{2, 1}}$-norm is applied to learn the optimal association matrix and cluster indicator matrix. Furthermore, to enhance the discriminability between bags, the proposed model enforces the similarity of the cluster indicators for the bag with the same label to approximate 1 and the similarity with different labels to 0. Finally, an iterative updating algorithm is derived to solve the proposed model. The experimental results show that the proposed model is superior to other multi-instance clustering models.

Key words: multi-instance learning, multi-view learning, concept factorization, multi-instance kernel function

CLC Number: 

  • TP391.4
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[1] Li Qi-xiang, Xiao Yan-shan, Hao Zhi-feng, Ruan Yi-bang. An Algorithm Based on Multi-task Multi-instance Anti-noise Learning [J]. Journal of Guangdong University of Technology, 2018, 35(03): 47-53.
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