半监督两个视角的多示例聚类模型

蔡昊; 刘波

doi:10.12052/gdutxb.200120

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半监督两个视角的多示例聚类模型

更新时间：2026-05-27

- 半监督两个视角的多示例聚类模型
- A Semi-supervised Two-view Multiple-Instance Clustering Model
- 广东工业大学学报 2021年38卷第3期页码：22-28, 47
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
  
  蔡昊(1992–)，男，博士研究生，主要研究方向为数据挖掘，E-mail：caiyh9658@163.com
  刘波(1978–)，男，教授，博士，主要研究方向为机器学习、数据挖掘，E-mail：csbliu@gmail.com
- 基金信息：
  
  国家自然科学基金资助项目(61876044)
- DOI：10.12052/gdutxb.200120
  中图分类号： TP391.4
- 网络首发：2021-05-10，
  
  纸质出版：2021
- 稿件说明：
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蔡昊, 刘波. 半监督两个视角的多示例聚类模型[J]. 广东工业大学学报, 2021, 38(3): 22-28, 47.

Cai Hao, Liu Bo. A Semi-supervised Two-view Multiple-Instance Clustering Model[J]. 2021, 38(3): 22-28, 47.
蔡昊, 刘波. 半监督两个视角的多示例聚类模型[J]. 广东工业大学学报, 2021, 38(3): 22-28, 47. DOI： 10.12052/gdutxb.200120.

Cai Hao, Liu Bo. A Semi-supervised Two-view Multiple-Instance Clustering Model[J]. 2021, 38(3): 22-28, 47. DOI： 10.12052/gdutxb.200120.

摘要

提出了一种新的半监督两个视角的多示例聚类模型

整合文本视角和图像视角解决了伴有少量标签的多示例图像聚类问题。提出的模型首先嵌入概念分解和多示例核成为一个整体

学习每个视角的关联矩阵和两个视角所共享的聚类指示矩阵。而后

应用

${l_{2

1}}$

img text_id='M1' class='formula-img' style='display:none;' src='gdgydxxb-38-3-22_M1.jpg'>

范数学习最优的关联矩阵和聚类指示矩阵。进一步地

为了增加包之间的判别力

提出的模型强迫相同标签包的聚类指示向量间的相似性趋于1

不同标签包的指示向量间的相似性趋于0。最后

给出一种迭代更新算法优化提出的模型。实验结果表明，提出的模型优于现有的多示例聚类模型。

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

${l_{2

1}}$

img text_id='M2' class='formula-img' style='display:none;' src='gdgydxxb-38-3-22_M2.jpg'>

-norm is applied to learn the optimal association matrix and cl

uster 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.

关键词

Keywords

references

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