[1]刘相男,丁世飞,王丽娟.基于深度图正则化矩阵分解的多视图聚类算法[J].智能系统学报,2022,17(1):158-169.[doi:10.11992/tis.202104046]
LIU Xiangnan,DING Shifei,WANG Lijuan.A multi-view clustering algorithm based on deep matrix factorization with graph regularization[J].CAAI Transactions on Intelligent Systems,2022,17(1):158-169.[doi:10.11992/tis.202104046]
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LIU Xiangnan,DING Shifei,WANG Lijuan.A multi-view clustering algorithm based on deep matrix factorization with graph regularization[J].CAAI Transactions on Intelligent Systems,2022,17(1):158-169.[doi:10.11992/tis.202104046]
基于深度图正则化矩阵分解的多视图聚类算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第1期
页码:
158-169
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2022-01-05
- Title:
- A multi-view clustering algorithm based on deep matrix factorization with graph regularization
- Keywords:
- multi-view clustering; deep matrix factorization; geometric structure; graph regularization; matrix factorization; multi-view representation learning; hierarchical structure information; deep learning
- 分类号:
- TP181
- 摘要:
- 针对现实社会中由多种表示或视图组成的多视图数据广泛存在的问题，深度矩阵分解模型因其能够挖掘数据的层次信息而备受关注,但该模型忽略了数据的几何结构信息。为解决以上问题，本文提出基于深度图正则化矩阵分解的多视图聚类算法，通过获取每个视图的局部结构信息和全局结构信息在逐层分解中加入两个图正则化限制，保护多视图数据的几何结构信息，同时将视图的权重与特征表示矩阵进行结合获得共识表示矩阵，最大化视角间的互补性，保证数据的一致性和差异性。除此之外，本文使用迭代更新变量的方法最小化目标函数，不断优化模型并进行收敛性分析。将本文算法和多个算法在三个人脸数据集和两个图像数据集上运行，通过多项指标的对比可以看出本文提出的算法具备良好的性能表现。
- Abstract:
- In view of the extensive multi-view data composed of multiple representations or views in real world, the deep matrix factorization (DMF) model has attracted much attention because of its ability to explore the hierarchical information of data. However, it ignores geometric structure of data. In order to solve the above problem, this paper proposes a multi-view clustering algorithm based on deep matrix factorization with graph regularization, which can protect geometric structure information of data by acquiring the local and global structure information of each view and adding two graph regularization limits in the layer-by-layer decomposition. It combines the weight of views with feature representation matrix to acquire consensus representation matrix to maximize complementarity of data and ensure consistency and difference among data. In addition, this paper uses the iterative updating variables method to minimize the objective function, continuously optimize model and conduct convergence analysis. This algorithm and other multiple algorithms are run on three face benchmark datasets and two image data sets. Through the comparison of multiple indicators, it can be seen that the algorithm proposed in this paper has good performance.
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备注/Memo
收稿日期:2021-04-28。
基金项目:国家自然科学基金项目(61976216,61672522).
作者简介:刘相男，硕士研究生，主要研究方向为数据挖掘、聚类分析、多视图聚类算法;丁世飞，教授，博士生导师，博士，CCF 杰出会员，第八届吴文俊人工智能科学技术奖获得者，主要研究方向为人工智能与模式识别、机器学习与数据挖掘。主持国家重点基础研究计划课题 1 项、国家自然科学基金面上项目 3 项。发表学术论文 200 余篇， 出版专著 5 部;王丽娟，副教授，CCF 会员，主要研究方向为机器学习、聚类分析。
通讯作者:丁世飞. E-mail: dingsf@cumt.edu.cn
更新日期/Last Update:
1900-01-01