多样性引导的深度多视图聚类算法  

Diversity Guided Deep Multi-view Clustering Algorithm

作　　者：胡虹 李学俊[1] 廖竞[1] HU Hong;LI Xue-Jun;LIAO Jing(School of Computer Science and Technology,Southwest University of Science and Technology,Mianyang 621010,China)

机构地区：[1]西南科技大学计算机科学与技术学院,绵阳621010

出　　处：《计算机系统应用》2024年第7期161-169,共9页Computer Systems & Applications

基　　金：国家自然科学基金(61872304)。

摘　　要：多视图聚类旨在从不同视图的多样性信息中,学习到更加全面和准确的共识表示,以提高模型的聚类性能.目前大部分多视图聚类算法采用希尔伯特-施密特独立性准则(HSIC)或自适应加权方法从全局考虑各视图的多样性,忽略了各视图样本之间的局部多样性信息学习.针对上述问题,提出了多样性引导的深度多视图聚类算法.首先,提出了融合多头自注意力机制的软聚类模块,多头自注意力机制用来学习全局多样性,软聚类模糊C均值算法用来学习局部多样性;其次,在深度图自编码器网络结构中引入软聚类模块,以达到多样性信息引导潜在表示生成的目的;然后,将得到的各视图潜在表示进行加权融合得到共识表示,并采用谱聚类算法对共识表示进行聚类;最后,在3个常用数据集上进行了对比实验和消融实验.实验结果表明,提出的聚类算法具有良好的聚类效果,以及提出的多样性信息学习模块可以有效提高算法聚类性能.Multi-view clustering aims to learn more comprehensive and accurate consensus representations from the diversity information of different views to improve the clustering performance of the model.Currently,most multi-view clustering algorithms use the Hilbert-Schmidt independence criterion(HSIC)or adaptive weighting method to consider the diversity of each view from a global perspective,ignoring the learning of local diversity information between samples in each view.This study proposes a diversity-guided deep multi-view clustering algorithm to address the above issues.Firstly,the study proposes a soft clustering module integrating multi-head self-attention mechanism.Specifically,the multi-head self-attention mechanism is applied to learn global diversity,and the soft clustering fuzzy C-means algorithm is utilized to learn local diversity.Secondly,a soft clustering module is introduced into the structure of the depth map auto-encoder network to generate potential representations guided by diversity information.Then,the obtained latent representations of each view are weighted and fused to obtain consensus representations,and the spectral clustering algorithm is leveraged to cluster the consensus representations.Finally,comparative experiments and ablation experiments are conducted on three commonly used datasets.The experimental results show that the proposed clustering algorithm has good clustering performance,and the diversity information learning module can effectively improve the clustering performance of the algorithm.

关 键 词：多视图聚类 深度聚类 软聚类 多头自注意力机制 多样性 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]