计算机工程2026，Vol.52Issue(5)：103-116,14.DOI:10.19678/j.issn.1000-3428.0070309
基于共识图学习的多任务多视图聚类
Consensus Graph Learning for Multi-Task Multi-View Clustering
摘要
Abstract
Multi-view clustering focuses on mining consistency information between different views to improve performance.Most existing multi-view clustering algorithms focus on single-task multi-view clustering while ignoring the similarity of related tasks,which results in poor performance on multiple tasks.Multi-task clustering can effectively handle the correlation between multiple tasks,and the most common clustering problem in practice is multi-task multi-view data clustering.To better explore the correlation between related tasks and obtain more effective consistency information from the multi-view data of each task,this paper proposes a multi-task multi-view clustering algorithm based on consensus graph learning.This algorithm establishes a view-specific shared feature library,which stores and migrates all tasks and potential information shared by all views,that is,the feature-embedding information shared by each task in the common view.When dealing with new tasks,each view of a new task optimizes the similar graph structure and corresponding sample embeddings simultaneously to obtain more accurate sample embedding representations.Meanwhile,collaborative clustering is introduced to achieve knowledge transfer between shared feature libraries and new task sample embeddings.This approach utilizes the diversity information of feature embedding to promote the consistent expression of various views in the new task,while updating the shared feature library based on the sample information of this new task.After obtaining the optimal sample-embedding representation,all views are fused to obtain a consensus graph for the new task.Subsequently,an alternating direction strategy is adopted to optimize the model,and rank constraints from the Laplacian matrix of the consensus graph are introduced to directly obtain the clustering results.The results of experiments show that,compared with six existing advanced algorithms,the proposed algorithm exhibits higher clustering performance and efficiency on five multi-task multi-view datasets.关键词
多视图聚类/多任务学习/共识表示/图学习/协同聚类Key words
multi-view clustering/multi-task learning/consensus representation/graph learning/co-clustering分类
信息技术与安全科学引用本文复制引用
王丽娟,李雪燕,尹明,郝志峰,蔡瑞初,陈薇,刘睿..基于共识图学习的多任务多视图聚类[J].计算机工程,2026,52(5):103-116,14.基金项目
国家自然科学基金(62206064,62376101) （62206064,62376101）
新一代人工智能国家科技重大专项(2021ZD0111501). （2021ZD0111501）
